Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Australia, between 2002 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/search.json b/docs/search.json
index 2091440..44c2557 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -53,14 +53,14 @@
"href": "tl2-can.html#overview",
"title": "Canada",
"section": "Overview",
- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n36,991,981 (Canada’s 2021 Census), 9,984,670 km2 (Geography (statcan.gc.ca))\n\n\n\n\nAdministrative structure \nFederal system of government; Principles respecting Canada's relationship with Indigenous peoples1\n\n\nRegional or state-level governments \n10 provinces and 3 territories2\n\n\nIntermediate-level governments \n--\n\n\nMunicipal-level governments \n3,888 (2021), includes regional governments and upper-tier municipalities\n\n\nShare of subnational government in total expenditure/revenues (2021)\n70.1% of total expenditure\n74.7% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nSupply chain and market access challenges; transition to net-zero emissions/greening the economy; climate change; business productivity gaps; technological change/digitization; tight labour markets/workforce of the future; development for rural and remote communities (for example, broadband); housing supply and affordability; inclusivity of under-represented groups (Indigenous, racialized).\n\n\nObjectives of regional policy\nPromote short- and long-term job creation, wage growth and economic development in all regions. This includes delivering regionally tailored programs, services, knowledge and expertise, so that all regions have access to place-based programming and support.\n\n\nLegal/institutional framework for regional policy\nCanadian Constitution, Section 36\nEnabling legislation for Canada’s regional development agencies:\n\nAtlantic Canada Opportunities Agency Act\nEconomic Development Agency of Canada for the Regions of Quebec Act\nWestern Economic Diversification Act3\nCanadian Northern Economic Development Agency\nFederal Economic Development Agency for Northern Ontario\nFederal Economic Development Agency for Southern Ontario\n\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nFinances for Canada’s regional development agencies4:\n\nAtlantic Canada Opportunities Agency\nCanada Economic Development for Quebec Regions\nCanadian Northern Economic Development Agency\nFederal Economic Development Agency for Southern Ontario\nFederal Economic Development Agency for Northern Ontario\nPrairies Economic Development Canada\nPacific Economic Development Canada5\n\nFiscal Equalisation Mechanisms between Jurisdictions:\n\nEqualization program\n\n\n\nNational regional development policy framework\nCanada’s federal government has seven regional development agencies (RDAs) that are responsible for economic development in their respective regions. They provide regionally tailored programs, services, knowledge, and expertise. RDAs engage with strategic partners at the regional level and across the federal government on an ongoing basis.\n\n\nUrban policy framework\n--\n\n\nRural policy framework\nReleased on June 27, 2019, the Rural Economic Development Strategy outlines a whole of government approach to meet the economic, social development, and sustainability needs of rural Canada, including connectivity, climate change mitigation and adaption, infrastructure, skills and labour, housing, and tourism.\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\n\nCanada's Regional Development Agencies, Regional Economic Growth Through Innovation and Community Futures Canada\nCanada's Connectivity Strategy\nUniversal Broadband Fund (UBF)\n\n\n\nPolicy co-ordination tools at national level\nCabinet Committee Mandate and Membership\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\nFederal/Provincial/Territorial First Ministers Conferences or Meetings (FMMs), the Canadian Intergovernmental Conference Secretariat (CICS), the Council of the Federation, and Federal/Provincial/Territorial (FPT) Agreements.\n\n\nPolicy co-ordination tools at regional level\nAtlantic Growth Strategy\n\n\nEvaluation and monitoring tools\nAnnual Plans and Results by Department\nGovernment of Canada Evaluation (5-year cycle)\n\n\nFuture orientations of regional policy\n--"
+ "text": "Overview\n\n\n\n Population and territory 36,991,981 (Canada’s 2021 Census), 9,984,670 km2 (Geography (statcan.gc.ca)) Administrative structure Federal system of government; Principles respecting Canada's relationship with Indigenous peoples1 Regional or state-level governments 10 provinces and 3 territories2 Intermediate-level governments -- Municipal-level governments 3,888 (2021), includes regional governments and upper-tier municipalities Share of subnational government in total expenditure/revenues (2021) 70.1% of total expenditure 74.7% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Supply chain and market access challenges; transition to net-zero emissions/greening the economy; climate change; business productivity gaps; technological change/digitization; tight labour markets/workforce of the future; development for rural and remote communities (for example, broadband); housing supply and affordability; inclusivity of under-represented groups (Indigenous, racialized). Objectives of regional policy Promote short- and long-term job creation, wage growth and economic development in all regions. This includes delivering regionally tailored programs, services, knowledge and expertise, so that all regions have access to place-based programming and support. Legal/institutional framework for regional policy Canadian Constitution, Section 36 Enabling legislation for Canada’s regional development agencies: Atlantic Canada Opportunities Agency Act Economic Development Agency of Canada for the Regions of Quebec Act Western Economic Diversification Act3 Canadian Northern Economic Development Agency Federal Economic Development Agency for Northern Ontario Federal Economic Development Agency for Southern Ontario Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) Finances for Canada’s regional development agencies4: Atlantic Canada Opportunities Agency Canada Economic Development for Quebec Regions Canadian Northern Economic Development Agency Federal Economic Development Agency for Southern Ontario Federal Economic Development Agency for Northern Ontario Prairies Economic Development Canada Pacific Economic Development Canada5 Fiscal Equalisation Mechanisms between Jurisdictions: Equalization program National regional development policy framework Canada’s federal government has seven regional development agencies (RDAs) that are responsible for economic development in their respective regions. They provide regionally tailored programs, services, knowledge, and expertise. RDAs engage with strategic partners at the regional level and across the federal government on an ongoing basis. Urban policy framework -- Rural policy framework Released on June 27, 2019, the Rural Economic Development Strategy outlines a whole of government approach to meet the economic, social development, and sustainability needs of rural Canada, including connectivity, climate change mitigation and adaption, infrastructure, skills and labour, housing, and tourism. Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Canada's Regional Development Agencies, Regional Economic Growth Through Innovation and Community Futures Canada Canada's Connectivity Strategy Universal Broadband Fund (UBF) Policy co-ordination tools at national level Cabinet Committee Mandate and Membership Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) Federal/Provincial/Territorial First Ministers Conferences or Meetings (FMMs), the Canadian Intergovernmental Conference Secretariat (CICS), the Council of the Federation, and Federal/Provincial/Territorial (FPT) Agreements. Policy co-ordination tools at regional level Atlantic Growth Strategy Evaluation and monitoring tools Annual Plans and Results by Department Government of Canada Evaluation (5-year cycle) Future orientations of regional policy --"
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- "text": "Regional inequality trends\nCanada experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2004. The figures are normalized, with values in the year 2000 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \nIn Canada, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 14% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Canada, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nCanada experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2004. The figures are normalized, with values in the year 2000 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Canada, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 14% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Canada, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n19,960,889 inhabitants (2023, including the Chilean Antarctic Territory1), 2,006,360 km²\n\n\n\n\nAdministrative structure \nUnitary\n\n\nRegional or state-level governments \n16 regions\n\n\nIntermediate-level governments \n56 provinces\n\n\nMunicipal-level governments \n346 communes and 345 municipalities (The municipality of Cabo de Hornos administers the communes of Cabo de Hornos and Antártica Chilena)\n\n\nShare of subnational government in total expenditure/revenues (2021)\n10.6% of total expenditure\n14.5% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nPresident Boric's government plan considers the deepening of decentralization as one of the four axes that run through his government program 2.\nWithin this framework, activities have been carried out with the Association of Regional Governors of Chile (AGORECHI) with whom an agreement was signed in January 2023 to advance in distributing power to the regions, particularly in matters of Interlevel Coordination and territorial participation, Administrative Decentralization, Fiscal Decentralization and the Formulation of a National Decentralization Policy. All this with the aim of providing public policies and programs more consistent with the needs of each region, closing territorial gaps, and deepening democracy and the proximity of citizens to decision-making.\nAs stated in said protocol, the Government is working on drawing up a National Decentralization Policy that develops some of these agreements, in accordance with certain guiding principles that the parties have agreed upon.\nThe aforementioned policy then proposes to address measures aimed at strengthening the Chilean decentralization process in a period of 10 to 12 years. Said policy is expected to indicate a distribution of powers, both interlevel and intersectoral, and establish the bases for effective administrative coordination.\nLikewise, the administration is engaged in the elaboration of a Regional and Municipal Revenue Bill that will mean a substantial advance in terms of fiscal decentralization.\nFinally, it should be noted that work is being done on a redesign of the Ministry of the Interior, which will surely generate relevant changes related to the representation of the President of the Republic in subnational territories, in the context of a Unitary State.\n\n\nObjectives of regional policy\nRegarding Interlevel Coordination and Territorial Participation:\n\nInterlevel coordination via meetings of nation-region cabinets integrated at the regional level with the Presidential Delegate, Governor and Seremis; and at the national level with the President, Ministers and Governors.\nInstall a Decentralization Roundtable with national and regional authorities, which defines prioritized measures and follows up on the different agreements adopted on decentralization.\nIssue presidential instructions that ensure that the various nation-region exchanges and efforts, not institutionalized by law or other normative instrument, are developed based on dialogue, mutual respect, and adequate articulation and interlevel coordination.\nInstall Civil Society Councils in all Regional Governments as established by Law No. 20,500 on Associations and Citizen Participation in public management.\n\nIn terms of Administrative Decentralization, in addition:\n\nImprove the regulatory framework that regulates the procedure for the transfer of powers.\nPromote processes of transfer of powers and asymmetric Decentralization prior agreement between each Regional Government and the executive.\nDevelop joint initiatives between the national government and regional governments that strengthen their progressive autonomy in terms of: accreditation of management quality, modernization of ICT-oriented management and processes, territorial analysis, internationalization, installation and start-up of the metropolitan areas, Advanced Human Capital Management System, among others.\n\nRegarding Fiscal Decentralization:\n\nStrengthen the participation of GOREs in the budget process\nReview and modify the regulations that regulate the budget structure of the Regional Governments and with their glosses (Decree No. 24 (2020) of the Ministry of the Interior and Public Security).\nSubmit to Parliament a Regional Revenue and Fiscal Decentralization Bill.\n\n\n\nLegal/institutional framework for regional policy\nWithin the framework established by the Political Constitution of the Republic3, the DFL1-19175 of the Ministry of the Interior that establishes the consolidated, coordinated, systematized and updated text of Law 19,175, Constitutional Organic Law on Government and Regional Administration4. Other relevant legal bodies are the DFL 1DFL 1-19653 that establishes the Consolidated, Coordinated and Systematized Text of Law No. 18,575, Constitutional Organic General Bases of the State Administration5 and the Decree Law 1263 Organic of State Financial Administration6 .\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nIn addition to the resources that are directly transferred to the budget item of the Regional Governments, and in the budgets of the decentralized Ministries and Public Services that operate in the territory, the 2023 Public Budget7 contemplates the allocation of:\n\n62 thousand 875 million Chilean pesos (approximately 79 million US$) for the Special Plans for Extreme Zones.\n7 thousand 298 million Chilean pesos (approximately 9 million US$) for the Development Plans for Lagging Territories.\n113 thousand 526 million Chilean pesos (approximately US$142 million) as a provision to the National Fund for Regional Development (FNDR). and to the Regional Contingency Fund in the budget of the Subsecretariat for Regional and Administrative Development, SUBDERE; of the Ministry of the Interior and Public Security and\n7 thousand 798 million Chilean pesos (approximately 10 million US$) for the execution of the Subnational Management Strengthening Program of SUBDERE.\n271 thousand 469 million Chilean pesos (approximately 339 million US$) for the execution of SUBDERE Local Development Programs\n\n\n\nNational regional development policy framework\nThe executive considers the elaboration of a New National Decentralization Policy\n\n\nUrban policy framework\nThe legal framework of the Urban Policy is established by Decree Force of Law 458 of the Ministry of Housing and Urbanism that approves the New General Law of Urbanism and Construction8 , and Decree 78 of the Ministry of Housing and Urbanism, published in March 2014, which approves the National Urban Development Policy and creates the National Urban Development Council9.\n\n\nRural policy framework\nDecree 19 of the Ministry of the Interior and Public Security, published on May 5, 2020 approves the National Rural Development Policy10.\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nThrough the National Fund for Regional Development (FNDR), the Central Government transfers resources to regions for the development of actions in the different areas of social, economic and cultural development of the Region in order to obtain harmonious and equitable territorial development.\nThe objective of the regional contingency fund is to finance expenses associated with contingent needs of the regions, in matters of rural infrastructure, enhancement of heritage, sanitary sanitation, solid waste, energization and support for subnational management, including support for the implementation of new competencies.\nThrough the Subnational Management Strengthening program, transfers are made to Municipalities and Other Public Entities for the development of human resource training initiatives, scholarships, modernization, support for management improvement and prevention, risk mitigation and disaster response, among other.\nThrough SUBDERE's Local Development Programs, transfers are made to Municipalities and Other Public Entities for the development of investment initiatives for Urban Improvement, Community Equipment, Neighborhood Improvement, City Recovery and responsible ownership of Pets, among other purposes.\nThe Special Plans for Extreme Zones have the objective of \"addressing the deficits in investment, in public infrastructure, and the difficulties of access to employment markets and services due to the low social profitability of this type of project in these zones in order to\" contribute to improve the quality of life of the inhabitants of extreme zones.\nThe Development Plans for Lagging Territories allocate resources for the acquisition of non-financial assets and the execution of investment programs and initiatives, approved by the respective regional governments, in the territories included in the Development Plan for Lagging Territories, established by Decree Supreme Court No. 975 of 2019 of the Ministry of the Interior and Public Security, and its modifications 11.\n\n\nPolicy co-ordination tools at national level\nThe President of the Republic leads the coordination of policies at the national level through various committee instances with his ministerial cabinet and coordination with political actors and other powers of the State.\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\nAt the political level, the Decentralization Forum is a collaborative instance of permanent work with the Regional Governors for political-technical dialogue that allows in the short term to advance towards an effectively decentralized Chile. The purpose of this instance is to agree on a common road map regarding the process of transferring powers from the national to the regional level.\n\n\nPolicy co-ordination tools at regional level\nThe budgetary process leading to the elaboration of the budget of\nThe public sector contemplates in its elaboration instances of regional coordination led by the Regional Government leading to the elaboration of the Regional Investment Draft.\n\n\nEvaluation and monitoring tools\nThe State Financial Administration Law assigns to the Budget Office of the Ministry of Finance (DIPRES) the function of \"guiding and regulating the budget formulation process\" and evaluating the programs and compliance with the purposes and goals set by the public services\nLaw No. 20,530 in its article 3 letter c) establishes that the Ministry of Social Development and Family (MDSyF) must evaluate and rule, through a recommendation report, on new social programs or that propose to be significantly reformulated, that are proposed by ministries or public services, in order to achieve coordination in the design of social policies. Likewise, letter d) of said article states that the aforementioned ministry must \"collaborate with the monitoring of the management and implementation of the social programs that are being executed by the public services related to or dependent on it and other ministries, through the evaluation and pronouncement through a follow-up report of, among others, its efficiency, its effectiveness and its targeting\"..\n\n\nFuture orientations of regional policy\nThe executive considers the elaboration of a National Decentralization Policy and the Proposal for a Law on Regional Income and Fiscal Decentralization."
+ "text": "Overview\n\n\n\n Population and territory 19,960,889 inhabitants (2023, including the Chilean Antarctic Territory1), 2,006,360 km² Administrative structure Unitary Regional or state-level governments 16 regions Intermediate-level governments 56 provinces Municipal-level governments 346 communes and 345 municipalities (The municipality of Cabo de Hornos administers the communes of Cabo de Hornos and Antártica Chilena) Share of subnational government in total expenditure/revenues (2021) 10.6% of total expenditure 14.5% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges President Boric's government plan considers the deepening of decentralization as one of the four axes that run through his government program 2. Within this framework, activities have been carried out with the Association of Regional Governors of Chile (AGORECHI) with whom an agreement was signed in January 2023 to advance in distributing power to the regions, particularly in matters of Interlevel Coordination and territorial participation, Administrative Decentralization, Fiscal Decentralization and the Formulation of a National Decentralization Policy. All this with the aim of providing public policies and programs more consistent with the needs of each region, closing territorial gaps, and deepening democracy and the proximity of citizens to decision-making. As stated in said protocol, the Government is working on drawing up a National Decentralization Policy that develops some of these agreements, in accordance with certain guiding principles that the parties have agreed upon. The aforementioned policy then proposes to address measures aimed at strengthening the Chilean decentralization process in a period of 10 to 12 years. Said policy is expected to indicate a distribution of powers, both interlevel and intersectoral, and establish the bases for effective administrative coordination. Likewise, the administration is engaged in the elaboration of a Regional and Municipal Revenue Bill that will mean a substantial advance in terms of fiscal decentralization. Finally, it should be noted that work is being done on a redesign of the Ministry of the Interior, which will surely generate relevant changes related to the representation of the President of the Republic in subnational territories, in the context of a Unitary State. Objectives of regional policy Regarding Interlevel Coordination and Territorial Participation: Interlevel coordination via meetings of nation-region cabinets integrated at the regional level with the Presidential Delegate, Governor and Seremis; and at the national level with the President, Ministers and Governors. Install a Decentralization Roundtable with national and regional authorities, which defines prioritized measures and follows up on the different agreements adopted on decentralization. Issue presidential instructions that ensure that the various nation-region exchanges and efforts, not institutionalized by law or other normative instrument, are developed based on dialogue, mutual respect, and adequate articulation and interlevel coordination. Install Civil Society Councils in all Regional Governments as established by Law No. 20,500 on Associations and Citizen Participation in public management. In terms of Administrative Decentralization, in addition: Improve the regulatory framework that regulates the procedure for the transfer of powers. Promote processes of transfer of powers and asymmetric Decentralization prior agreement between each Regional Government and the executive. Develop joint initiatives between the national government and regional governments that strengthen their progressive autonomy in terms of: accreditation of management quality, modernization of ICT-oriented management and processes, territorial analysis, internationalization, installation and start-up of the metropolitan areas, Advanced Human Capital Management System, among others. Regarding Fiscal Decentralization: Strengthen the participation of GOREs in the budget process Review and modify the regulations that regulate the budget structure of the Regional Governments and with their glosses (Decree No. 24 (2020) of the Ministry of the Interior and Public Security). Submit to Parliament a Regional Revenue and Fiscal Decentralization Bill. Legal/institutional framework for regional policy Within the framework established by the Political Constitution of the Republic3, the DFL1-19175 of the Ministry of the Interior that establishes the consolidated, coordinated, systematized and updated text of Law 19,175, Constitutional Organic Law on Government and Regional Administration4. Other relevant legal bodies are the DFL 1DFL 1-19653 that establishes the Consolidated, Coordinated and Systematized Text of Law No. 18,575, Constitutional Organic General Bases of the State Administration5 and the Decree Law 1263 Organic of State Financial Administration6 . Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) In addition to the resources that are directly transferred to the budget item of the Regional Governments, and in the budgets of the decentralized Ministries and Public Services that operate in the territory, the 2023 Public Budget7 contemplates the allocation of: 62 thousand 875 million Chilean pesos (approximately 79 million US$) for the Special Plans for Extreme Zones. 7 thousand 298 million Chilean pesos (approximately 9 million US$) for the Development Plans for Lagging Territories. 113 thousand 526 million Chilean pesos (approximately US$142 million) as a provision to the National Fund for Regional Development (FNDR). and to the Regional Contingency Fund in the budget of the Subsecretariat for Regional and Administrative Development, SUBDERE; of the Ministry of the Interior and Public Security and 7 thousand 798 million Chilean pesos (approximately 10 million US$) for the execution of the Subnational Management Strengthening Program of SUBDERE. 271 thousand 469 million Chilean pesos (approximately 339 million US$) for the execution of SUBDERE Local Development Programs National regional development policy framework The executive considers the elaboration of a New National Decentralization Policy Urban policy framework The legal framework of the Urban Policy is established by Decree Force of Law 458 of the Ministry of Housing and Urbanism that approves the New General Law of Urbanism and Construction8 , and Decree 78 of the Ministry of Housing and Urbanism, published in March 2014, which approves the National Urban Development Policy and creates the National Urban Development Council9. Rural policy framework Decree 19 of the Ministry of the Interior and Public Security, published on May 5, 2020 approves the National Rural Development Policy10. Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Through the National Fund for Regional Development (FNDR), the Central Government transfers resources to regions for the development of actions in the different areas of social, economic and cultural development of the Region in order to obtain harmonious and equitable territorial development. The objective of the regional contingency fund is to finance expenses associated with contingent needs of the regions, in matters of rural infrastructure, enhancement of heritage, sanitary sanitation, solid waste, energization and support for subnational management, including support for the implementation of new competencies. Through the Subnational Management Strengthening program, transfers are made to Municipalities and Other Public Entities for the development of human resource training initiatives, scholarships, modernization, support for management improvement and prevention, risk mitigation and disaster response, among other. Through SUBDERE's Local Development Programs, transfers are made to Municipalities and Other Public Entities for the development of investment initiatives for Urban Improvement, Community Equipment, Neighborhood Improvement, City Recovery and responsible ownership of Pets, among other purposes. The Special Plans for Extreme Zones have the objective of \"addressing the deficits in investment, in public infrastructure, and the difficulties of access to employment markets and services due to the low social profitability of this type of project in these zones in order to\" contribute to improve the quality of life of the inhabitants of extreme zones. The Development Plans for Lagging Territories allocate resources for the acquisition of non-financial assets and the execution of investment programs and initiatives, approved by the respective regional governments, in the territories included in the Development Plan for Lagging Territories, established by Decree Supreme Court No. 975 of 2019 of the Ministry of the Interior and Public Security, and its modifications 11. Policy co-ordination tools at national level The President of the Republic leads the coordination of policies at the national level through various committee instances with his ministerial cabinet and coordination with political actors and other powers of the State. Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) At the political level, the Decentralization Forum is a collaborative instance of permanent work with the Regional Governors for political-technical dialogue that allows in the short term to advance towards an effectively decentralized Chile. The purpose of this instance is to agree on a common road map regarding the process of transferring powers from the national to the regional level. Policy co-ordination tools at regional level The budgetary process leading to the elaboration of the budget of The public sector contemplates in its elaboration instances of regional coordination led by the Regional Government leading to the elaboration of the Regional Investment Draft. Evaluation and monitoring tools The State Financial Administration Law assigns to the Budget Office of the Ministry of Finance (DIPRES) the function of \"guiding and regulating the budget formulation process\" and evaluating the programs and compliance with the purposes and goals set by the public services Law No. 20,530 in its article 3 letter c) establishes that the Ministry of Social Development and Family (MDSyF) must evaluate and rule, through a recommendation report, on new social programs or that propose to be significantly reformulated, that are proposed by ministries or public services, in order to achieve coordination in the design of social policies. Likewise, letter d) of said article states that the aforementioned ministry must \"collaborate with the monitoring of the management and implementation of the social programs that are being executed by the public services related to or dependent on it and other ministries, through the evaluation and pronouncement through a follow-up report of, among others, its efficiency, its effectiveness and its targeting\".. Future orientations of regional policy The executive considers the elaboration of a National Decentralization Policy and the Proposal for a Law on Regional Income and Fiscal Decentralization."
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- "text": "Regional inequality trends\nChile experienced a decline in the Theil index of GDP per capita over 2008-2020. Inequality reached its maximum in 2010. The figures are normalized, with values in the year 2008 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \nIn Chile, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2013 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 5%, while it increased by 15% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Chile, between 2013 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector remained approximately stable across all regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nChile experienced a decline in the Theil index of GDP per capita over 2008-2020. Inequality reached its maximum in 2010. The figures are normalized, with values in the year 2008 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Chile, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2013 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 5%, while it increased by 15% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Chile, between 2013 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector remained approximately stable across all regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"title": "Japan",
"section": "Overview",
- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n125 308 652 (January 1, 2022), 377,973.26km2\n\n\n\n\nAdministrative structure \nUnitary country\n\n\nRegional or state-level governments \n1 Metropolis, 1 Province, 45 Prefectures\n\n\nIntermediate-level governments \n―\n\n\nMunicipal-level governments \n1 724 Municipalities\n\n\nShare of subnational government in total expenditure/revenues (2021)\n41.9% of total expenditure\n50.3% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nPopulation decline, declining birthrate and aging population, and hollowing out of regional industries, etc.\n\n\nObjectives of regional policy\n“The Vision for a Digital Garden City Nation” aims to solve rural issues and improve rural attractiveness while utilizing strength of each region through digital technologies, and to realize a society where everyone can live conveniently and comfortably wherever they live in Japan.\n\n\nLegal/institutional framework for regional policy\nNational Spatial Planning Act (1950, largely amended in 2005)\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\n\n\n\nNational regional development policy framework\n\n\n\nUrban policy framework\nCity Planning Act (1968), Urban Renaissance Special Measures Law (2014)\n\n\nRural policy framework\nThe Basic Law on Food, Agriculture and Rural Areas (1999)\nThe Basic Plan for Food, Agriculture and Rural Areas (2020)\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nNational Spatial Strategy(Regional Plan)\n\n\nPolicy co-ordination tools at national level\n\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\n\n\nPolicy co-ordination tools at regional level\n\n\n\nEvaluation and monitoring tools\n\n\n\nFuture orientations of regional policy"
+ "text": "Overview\n\n\n\n Population and territory 125 308 652 (January 1, 2022), 377,973.26km2 Administrative structure Unitary country Regional or state-level governments 1 Metropolis, 1 Province, 45 Prefectures Intermediate-level governments ― Municipal-level governments 1 724 Municipalities Share of subnational government in total expenditure/revenues (2021) 41.9% of total expenditure 50.3% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Population decline, declining birthrate and aging population, and hollowing out of regional industries, etc. Objectives of regional policy “The Vision for a Digital Garden City Nation” aims to solve rural issues and improve rural attractiveness while utilizing strength of each region through digital technologies, and to realize a society where everyone can live conveniently and comfortably wherever they live in Japan. Legal/institutional framework for regional policy National Spatial Planning Act (1950, largely amended in 2005) Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) National regional development policy framework Urban policy framework City Planning Act (1968), Urban Renaissance Special Measures Law (2014) Rural policy framework The Basic Law on Food, Agriculture and Rural Areas (1999) The Basic Plan for Food, Agriculture and Rural Areas (2020) Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) National Spatial Strategy(Regional Plan) Policy co-ordination tools at national level Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) Policy co-ordination tools at regional level Evaluation and monitoring tools Future orientations of regional policy"
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- "text": "Regional inequality trends\nJapan experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2007. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.066 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.01 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.13. For reference, the same value for OECD was 1.475. This gap decreased by 0.04 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.101. For reference, the same value for OECD was 1.325. This gap increased by 0.001 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.045 in 2020 and increased by 0.026 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nJapan experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2007. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.066 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.01 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.13. For reference, the same value for OECD was 1.475. This gap decreased by 0.04 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.101. For reference, the same value for OECD was 1.325. This gap increased by 0.001 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.045 in 2020 and increased by 0.026 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022)."
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"title": "Korea",
"section": "Overview",
- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n51,439,038(as of December 31, 2022), 100,432 ㎢.\n\n\n\n\nAdministrative structure\nUnitary\n\n\nRegional or state-level governments\n1 Special Metropolitan City, 6 Metropolitan Cities, 1 Special Self-Governing City, 3 Special Self-Governing Provinces and 6 Dos.\n\n\nIntermediate-level governments\n--\n\n\nMunicipal-level governments\n226 Municipalities (Si, Gun, and Gu).\n\n\nShare of subnational government in total expenditure/revenues (2021)\n44.2% of total expenditure\n45.6% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\n\nPopulation concentration in urban areas (especially in the Seoul metropolitan area and large cities), depulation in rural areas and small and medium-sized cities\nPolarization of income, assets, generations, classes and regions\nSevere disparity between cities and provinces in the medical care, education, green spaces, and cultural facilities\n\n\n\nObjectives of regional policy\n\nBalanced development and utilization of the land(Constitution)\nCreating the basis for ensuring the well-balanced development\nRedressing imbalance between regions\n\n\n\nLegal/institutional framework for regional policy\n\nConstitution of the Republic of Korea, Article 120, paragraph 2\nFramework Act on the National Land, Article 3, paragraph 2\nSpecial Act on Balanced National Development, Article 1\n\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\n\nThe Special Accounts for Balanced National Development : 10.8 trillion KRW (2.38 % of total central government expenditures in 2022 budget)\n\n\nThe “Special Accounts for Balanced National Development” system administered by the Ministry of Strategy and Finance\n\n\nLocal allocation tax and National government subsidy: 131.7 trillion KRW\n\n\n\nNational regional development policy framework\n\nComprehensive national land plan and Five-year balanced national development plan\n\n5th Comprehensive national land plan(2020-2040)\n5th Balanced national development plan(2023-2027) in the process of establishment\nThe 1st Balanced national development plan in 2004\n\n\n\n\nUrban policy framework\nBasic policies for national urban regeneration\n\n\nRural policy framework\nBasic policies for restructuring and regeneration of rural spaces\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\n\nSpecial accounts for balanced national development\nSpecial accounts for transportation facility\nGrowth hubs for balanced national development(the administrative city, innovation cities, enterprise cities, and free economic zones)\nSupra-regional cooperative projects\nDevelopment of growth promotion areas\nNational innovation clusters\nDevelopment of less favored area (depopulation regions, border regions, underdeveloped island regions and growth promoted districts)\n\n\n\nPolicy co-ordination tools at national level\nPresidential Committee for Balanced National Development\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\nCentral regional cooperation council\nRegional development investment agreement\n\n\n\nPolicy co-ordination tools at regional level\nCity/Do regional innovation council\n\n\nEvaluation and monitoring tools\nAnnual reports on balanced national development plans\n\n\nFuture orientations of regional policy\nThe 5th balanced national development plan under development sets out four major strategies covering education, industry, culture, welfare, and environment:\n\nEducation: Free education special zone, regulatory improvement, local university start-up and educational innovation, etc\nInnovative growth: Opportunity Development Zones(ODZ), relocation of public institutions, creation of specialized industries and start-up ecosystems\nLocal commitment: Securing a regionally-led development path, encompassing society, culture, and transportation\nEqual opportunity: Strengthening digital capabilities, responding to depopulation and improving conditions such as environment and welfare"
+ "text": "Overview\n\n\n\n Population and territory 51,439,038(as of December 31, 2022), 100,432 ㎢. Administrative structure Unitary Regional or state-level governments 1 Special Metropolitan City, 6 Metropolitan Cities, 1 Special Self-Governing City, 3 Special Self-Governing Provinces and 6 Dos. Intermediate-level governments -- Municipal-level governments 226 Municipalities (Si, Gun, and Gu). Share of subnational government in total expenditure/revenues (2021) 44.2% of total expenditure 45.6% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Population concentration in urban areas (especially in the Seoul metropolitan area and large cities), depulation in rural areas and small and medium-sized cities Polarization of income, assets, generations, classes and regions Severe disparity between cities and provinces in the medical care, education, green spaces, and cultural facilities Objectives of regional policy Balanced development and utilization of the land(Constitution) Creating the basis for ensuring the well-balanced development Redressing imbalance between regions Legal/institutional framework for regional policy Constitution of the Republic of Korea, Article 120, paragraph 2 Framework Act on the National Land, Article 3, paragraph 2 Special Act on Balanced National Development, Article 1 Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) The Special Accounts for Balanced National Development : 10.8 trillion KRW (2.38 % of total central government expenditures in 2022 budget) The “Special Accounts for Balanced National Development” system administered by the Ministry of Strategy and Finance Local allocation tax and National government subsidy: 131.7 trillion KRW National regional development policy framework Comprehensive national land plan and Five-year balanced national development plan 5th Comprehensive national land plan(2020-2040) 5th Balanced national development plan(2023-2027) in the process of establishment The 1st Balanced national development plan in 2004 Urban policy framework Basic policies for national urban regeneration Rural policy framework Basic policies for restructuring and regeneration of rural spaces Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Special accounts for balanced national development Special accounts for transportation facility Growth hubs for balanced national development(the administrative city, innovation cities, enterprise cities, and free economic zones) Supra-regional cooperative projects Development of growth promotion areas National innovation clusters Development of less favored area (depopulation regions, border regions, underdeveloped island regions and growth promoted districts) Policy co-ordination tools at national level Presidential Committee for Balanced National Development Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) Central regional cooperation council Regional development investment agreement Policy co-ordination tools at regional level City/Do regional innovation council Evaluation and monitoring tools Annual reports on balanced national development plans Future orientations of regional policy The 5th balanced national development plan under development sets out four major strategies covering education, industry, culture, welfare, and environment: Education: Free education special zone, regulatory improvement, local university start-up and educational innovation, etc Innovative growth: Opportunity Development Zones(ODZ), relocation of public institutions, creation of specialized industries and start-up ecosystems Local commitment: Securing a regionally-led development path, encompassing society, culture, and transportation Equal opportunity: Strengthening digital capabilities, responding to depopulation and improving conditions such as environment and welfare"
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- "text": "Regional inequality trends\nKorea experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2011. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.007 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.055 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 0.736. For reference, the same value for OECD was 1.475. This gap increased by 0.04 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 0.83. For reference, the same value for OECD was 1.325. This gap decreased by 0.092 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nKorea experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2011. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.007 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.055 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 0.736. For reference, the same value for OECD was 1.475. This gap increased by 0.04 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 0.83. For reference, the same value for OECD was 1.325. This gap decreased by 0.092 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022)."
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"title": "Latvia",
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n1 875 757 (early 2022) and 64 589 km²\n\n\n\n\nAdministrative structure \nUnitary\n\n\nRegional or state-level governments \n5\n\n\nIntermediate-level governments \n0\n\n\nMunicipal-level governments \n43\n\n\nShare of subnational government in total expenditure/revenues (2021)\n24.5% of total expenditure\n28.5% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nRegional disparities\n\n\nObjectives of regional policy\nRegional policy aims at developing the potential of all regions and reducing socio-economic disparities by strengthening their internal and external competitiveness, as well as providing solutions tailored to the specificities of territories for development of population and quality living environment.\n\n\nLegal/institutional framework for regional policy Ministry of Environmental Protection and Regional Development\nInstitution responsible for development and implementation of regional policy is Ministry of Environmental Protection and Regional Development. Municipalities and planning regions are involved in the elaboration and implementation of regional policy.\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nProposal prepared by Ministry of Environmental Protection and Regional Development envisage provision of EU funding of 2021-2027 planning period amounting to 1 021 909 159 Euros to measures to be implemented according to place-based approach. Amount of funding can be altered in the coordination process with sectoral ministries.\nFISCAL EQUALISATION MECHANISMS BETWEEN JURISDICTIONS\nIn 2023, local governments are provided with an increase in equalized revenues by 15.2% on average. The total amount of the local government financial equalization fund, comparing to 2022, has increased by 15.6% or 33.2 million euros. No municipality has a reduction in equalized revenues comparing to previous year.\n\nThe purpose of the local government equalization is, taking into consideration the socioeconomic differences between local governments, to create similar possibilities for local governments to perform their functions laid down in law, as well as to promote their initiative and independence in the creation of their financial resources.\nRevenue of the equalisation fund shall consist of the local government payments specified as a result of calculation of the equalisation of local government finances and of the State budget grant.\nThe assessed revenue of each local government and the criteria characterising local government expenditure shall be used for calculating the equalisation of local government finances.\nThe assessed revenue of a local government consist of the revenue from the immovable property tax forecasted by local government, and the share of the allocation of the revenue from the personal income tax determined for the budgets of local governments in the Annual State Budget Law.\nThe number of equalising units are used for calculating the equalisation of local government finances which includes information regarding local government expenditure related to the criteria characterising local government expenditure. The calculated number of equalising units for each local government include demographic and territorial differences of the particular local government.\nCalculation of the equalisation of local government finances shall be performed, taking into account the following principles:\na local government whose assessed revenue per one equalising unit is smaller than the average assessed revenue per one equalising unit in local governments in total receives a grant from the equalisation fund which is 60 percent of the difference between the average assessed revenues and the assessed revenues in a particular municipality per one equalising unit .\nlocal government whose assessed revenue per one equalising unit is larger than the average assessed revenue per one equalising unit in local governments in total makes a payment into the equalisation fund which is 60 percent of the difference between the average assessed revenues and the assessed revenues in a particular municipality per one equalising unit .\nthe difference between the assessed revenue of a local government per one equalising unit and the assessed revenue of the local government per one equalising unit which has the largest revenue shall be reduced proportionally by means of the State budget grant.\nAs a result of the equalisation of local government finances the equalised revenue of a local government shall consist of the assessed revenue of the local government reduced by the calculated payment into the equalisation fund or increased by the calculated grant from the equalisation fund.\n\n\n\nNational regional development policy framework\nRegional policy is defined in regional policy strategy document Regional Policy Guidelines 2021-2027.\n\n\nUrban policy framework\nUrban policy is part of regional policy, there are no separate documents for urban policy.\n\n\nRural policy framework\nRural policy is part of regional policy, described in Regional Policy Guidelines 2021-2027.\nAdditionally, rural policy is included in:\n\nCAP Strategic Plan 2023-2027 (Ministry of Agriculture)\nProgramme for Fisheries Development 2021-2027\nLocal area development strategies prepared by Local action groups\n\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nRegional policy includes measures co-financed by funds of European Union Cohesion policy.\n\n\nPolicy co-ordination tools at national level\nRegional Policy Guidelines 2021-2027 were discussed with sectoral ministries, planning regions and municipalities. Ministry of Environmental Protection and Regional Development cooperates with other ministries concerning regional policy issues.\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\n\n\nPolicy co-ordination tools at regional level\nPlanning regions are involved in the elaboration and implementation of regional policy\n\n\nEvaluation and monitoring tools\nThere are indicators defined in Regional Policy Guidelines 2021-2027\n\n\nFuture orientations of regional policy\nRegional Policy Guidelines 2021-2027 specify regional policy activities until 2027."
+ "text": "Overview\n\n\n\n Population and territory 1 875 757 (early 2022) and 64 589 km² Administrative structure Unitary Regional or state-level governments 5 Intermediate-level governments 0 Municipal-level governments 43 Share of subnational government in total expenditure/revenues (2021) 24.5% of total expenditure 28.5% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Regional disparities Objectives of regional policy Regional policy aims at developing the potential of all regions and reducing socio-economic disparities by strengthening their internal and external competitiveness, as well as providing solutions tailored to the specificities of territories for development of population and quality living environment. Legal/institutional framework for regional policy Ministry of Environmental Protection and Regional Development Institution responsible for development and implementation of regional policy is Ministry of Environmental Protection and Regional Development. Municipalities and planning regions are involved in the elaboration and implementation of regional policy. Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) Proposal prepared by Ministry of Environmental Protection and Regional Development envisage provision of EU funding of 2021-2027 planning period amounting to 1 021 909 159 Euros to measures to be implemented according to place-based approach. Amount of funding can be altered in the coordination process with sectoral ministries. FISCAL EQUALISATION MECHANISMS BETWEEN JURISDICTIONS In 2023, local governments are provided with an increase in equalized revenues by 15.2% on average. The total amount of the local government financial equalization fund, comparing to 2022, has increased by 15.6% or 33.2 million euros. No municipality has a reduction in equalized revenues comparing to previous year. The purpose of the local government equalization is, taking into consideration the socioeconomic differences between local governments, to create similar possibilities for local governments to perform their functions laid down in law, as well as to promote their initiative and independence in the creation of their financial resources. Revenue of the equalisation fund shall consist of the local government payments specified as a result of calculation of the equalisation of local government finances and of the State budget grant. The assessed revenue of each local government and the criteria characterising local government expenditure shall be used for calculating the equalisation of local government finances. The assessed revenue of a local government consist of the revenue from the immovable property tax forecasted by local government, and the share of the allocation of the revenue from the personal income tax determined for the budgets of local governments in the Annual State Budget Law. The number of equalising units are used for calculating the equalisation of local government finances which includes information regarding local government expenditure related to the criteria characterising local government expenditure. The calculated number of equalising units for each local government include demographic and territorial differences of the particular local government. Calculation of the equalisation of local government finances shall be performed, taking into account the following principles: a local government whose assessed revenue per one equalising unit is smaller than the average assessed revenue per one equalising unit in local governments in total receives a grant from the equalisation fund which is 60 percent of the difference between the average assessed revenues and the assessed revenues in a particular municipality per one equalising unit . local government whose assessed revenue per one equalising unit is larger than the average assessed revenue per one equalising unit in local governments in total makes a payment into the equalisation fund which is 60 percent of the difference between the average assessed revenues and the assessed revenues in a particular municipality per one equalising unit . the difference between the assessed revenue of a local government per one equalising unit and the assessed revenue of the local government per one equalising unit which has the largest revenue shall be reduced proportionally by means of the State budget grant. As a result of the equalisation of local government finances the equalised revenue of a local government shall consist of the assessed revenue of the local government reduced by the calculated payment into the equalisation fund or increased by the calculated grant from the equalisation fund. National regional development policy framework Regional policy is defined in regional policy strategy document Regional Policy Guidelines 2021-2027. Urban policy framework Urban policy is part of regional policy, there are no separate documents for urban policy. Rural policy framework Rural policy is part of regional policy, described in Regional Policy Guidelines 2021-2027. Additionally, rural policy is included in: CAP Strategic Plan 2023-2027 (Ministry of Agriculture) Programme for Fisheries Development 2021-2027 Local area development strategies prepared by Local action groups Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Regional policy includes measures co-financed by funds of European Union Cohesion policy. Policy co-ordination tools at national level Regional Policy Guidelines 2021-2027 were discussed with sectoral ministries, planning regions and municipalities. Ministry of Environmental Protection and Regional Development cooperates with other ministries concerning regional policy issues. Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) Policy co-ordination tools at regional level Planning regions are involved in the elaboration and implementation of regional policy Evaluation and monitoring tools There are indicators defined in Regional Policy Guidelines 2021-2027 Future orientations of regional policy Regional Policy Guidelines 2021-2027 specify regional policy activities until 2027."
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- "text": "Regional inequality trends\nLatvia experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2006. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.018 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.083 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 2.065. For reference, the same value for OECD was 1.325. This gap decreased by 0.057 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.065 in 2020 and decreased by 0.057 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Latvia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 79%, 42 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Latvia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nLatvia experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2006. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.018 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.083 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 2.065. For reference, the same value for OECD was 1.325. This gap decreased by 0.057 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.065 in 2020 and decreased by 0.057 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Latvia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 79%, 42 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Latvia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n2 805 998 (as of January 1, 2022), 65 284 km²\n\n\n\n\nAdministrative structure \nUnitary country\n\n\nRegional or state-level governments \n-\n\n\nIntermediate-level governments \n10 Regional Development Councils (joint municipal cooperation body)\n\n\nMunicipal-level governments \n60 Municipalities\n\n\nShare of subnational government in total expenditure/revenues (2021)\n24.2% of total expenditure\n25.8% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\n\nDifferent regional economic growth potential and uneven economic development\nCertain regions in Lithuania are at risk of greater poverty and social exclusion\nAn insufficiently sustainable environment, which negatively affects the attractiveness of the regions\n\n\n\nObjectives of regional policy\nObjectives of national regional policy (Law on Regional development) are:\n\nPromote the adaptation of regions to the changing conditions of the economic and social environment by exploiting and strengthening the competitive advantage and competence of each region\nIncrease the efficiency of the infrastructure and/or service network of functional zones, ensure that all residents could use this infrastructure and services\nReduce social and economic disparities across and within the regions.\n\n\n\nLegal/institutional framework for regional policy\n\nThe Law on Regional Development\nThe Law on Strategic Management\nThe Strategic Management Methodology\n\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nBudget:\n\nRegional Development Programme for 2022-2030 (RDP) and Programme for the European Union funds’ investments in 2021-2027 for the implementation of Regional Development Plans (€1623.9 million of EU Structural Funds (ERDF, CF, ESF+) and €378.1 million of national co-financing.)\n\nFiscal equalisation mechanism between the state and municipalities:\n\nThe Law on Methodology of Determination of Municipal Revenue, Articles 6-8.\n\n\n\nNational regional development policy framework\n\nThe National Progress Plan (NPP) – national document that identifies the main strategic goals and objectives to be achieved in all public policy areas. Strategic objective of the NPP #7: Sustainable and balanced development of the territory of Lithuania and reduction of regional exclusion. The basis for the Regional Development Programme (RDP).\nThe Regional Development Programme for 2022–2030 (RDP) – based on strategic objectives, identified in the NPP, indicates the directions of implementation of objectives, for which Regional Development Councils and/or municipalities are responsible along to the competence established in regulations.\n10 Regional Development Plans (RDPLs) – each region represented by Regional Development Council identifies social, economic and environmental problems and their causes within the region, determines the goals, objectives of regional development and indicators of monitoring, and plans the progress measures and preliminary funds.\n\n\n\nUrban policy framework\n\nObjectives of a national urban policy are integrated into the National Progress Plan (NPP) and the Regional Development Programme for 2022–2030 (RPP)\n\n\n\nRural policy framework\n\nObjectives of a national rural policy are integrated into the National Progress Plan (NPP) and the Regional Development Programme for 2022–2030 (RPP)\nCAP Strategic Plan of Lithuania for 2023–2027\n\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\n\nEuropean Structural Funds and national co-funding (27% of EUSF dedicated to Regional Development Programme for 2022–2030 and Regional Development Plans)\nThe Regional Development Programme for 2022–2030 (RDP)\nThe Regional Development Plans (RDPLs)\n\n\n\nPolicy co-ordination tools at national level\n\nMinistry of Interior (national regional policy formation, organization, coordination and monitoring of its implementation)\nNational Regional Development Council (it is the collegial advisory body for the Government and the Ministry of Interior in the area of national regional policy formulation and implementation. It consists from representatives of ministries, public authorities, the Association of Local Authorities in Lithuania, employers’ and trade unions’ organizations selected to the Tripartite Council, representatives of the Council of Non-Governmental Organisations and the National Council of Community Organizations, chairs of Regional Development Councils (RDCs)).\n\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\nNational Regional Development Council (It is empowered to discuss and consider the projects of planning documents being approved by the Government, including the Regional Development Programme, and regulations that may affect the regional development, and provide conclusions on these projects, also consider the progress of the implementation of these planning documents and, if necessary, submit proposals to the Government and the Ministry of Interior for the improvement of these documents, and consider other issues in the area of national regional policy formation and implementation)\n\n\n\nPolicy co-ordination tools at regional level\n\n10 Regional Development Councils (RDCs), one Council in each region, are legal entities established through an agreement between municipalities. RDCs are supra-municipal institutions. The body of RDC is the General meeting of participants; the governing bodies are the Panel (composed of the Mayors and members of the Municipal Councils) and the Administrative Director of RDC. Representing the region, their main competencies include: to plan and coordinate the implementation of the national regional policy in their respective region; to encourage social, economic development of the region, sustainable development of urbanised territories, decrease social and economic disparities within and across regions; and to encourage cooperation among municipalities in order to increase the efficiency of public services provision. The Administrative Director of RDC with small team (4-6 persons) acts as the secretariat of RDC. Also, RDC has an advisory Partner Group, that are engaged in delivering conclusions and opinions to the Panel regarding the projects of planning documents and other issues in the area of regional policy.\n10 Regional Development Plans (RDPLs) prepared and approved by Regional Development Councils\nSustainable Urban Development Strategies are prepared and implemented by urban municipalities in order to achieve the sustainable development of regional centres by solving social, economic, environmental and climate change challenges and in compliance with the principles of integrated approach.\nAgreements on Functional Zones among municipalities and Strategies for Sustainable Development of Functional Zones. Functional Zones are established and their strategies are prepared and implemented in order to increase the efficiency of the municipalities’ infrastructure and/or service network, to ensure the access to this infrastructure and services for all functional zone residents, and to create conditions for the joint actions of several municipalities and the implementation of joint investment projects.\n\n\n\nEvaluation and monitoring tools\n\nMinistry of Interior (monitoring the progress of implementation of the Regional Development Programme for 2022-2030, consulting RDCs on the consistency of RDPLs with the aim and objectives of the national regional policy, other issues of RDPLs preparation, implementation and monitoring in the area of Ministry competence)\nCentral Project Management Agency (methodical guidance for strategic planning, project management, monitoring, supervision, evaluation, financial management, progress reporting)\nSystem of indicators and targets along to the 2021-2030 National Progress Plan (NPP), the Regional Development Programme for 2022–2030 and 10 Regional Development Plans\n\n\n\nFuture orientations of regional policy\nFuture orientations of regional policy focus on following issues:\n\nDecrease interregional social and economic disparities by strengthening regions in most vulnerable areas in terms of target groups, public services, transportation and environmental issues.\nDecrease intraregional social and economic disparities by implementing the measures within the region towards economic, social or environmental issues in particular territory."
+ "text": "Overview\n\n\n\n Population and territory 2 805 998 (as of January 1, 2022), 65 284 km² Administrative structure Unitary country Regional or state-level governments - Intermediate-level governments 10 Regional Development Councils (joint municipal cooperation body) Municipal-level governments 60 Municipalities Share of subnational government in total expenditure/revenues (2021) 24.2% of total expenditure 25.8% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Different regional economic growth potential and uneven economic development Certain regions in Lithuania are at risk of greater poverty and social exclusion An insufficiently sustainable environment, which negatively affects the attractiveness of the regions Objectives of regional policy Objectives of national regional policy (Law on Regional development) are: Promote the adaptation of regions to the changing conditions of the economic and social environment by exploiting and strengthening the competitive advantage and competence of each region Increase the efficiency of the infrastructure and/or service network of functional zones, ensure that all residents could use this infrastructure and services Reduce social and economic disparities across and within the regions. Legal/institutional framework for regional policy The Law on Regional Development The Law on Strategic Management The Strategic Management Methodology Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) Budget: Regional Development Programme for 2022-2030 (RDP) and Programme for the European Union funds’ investments in 2021-2027 for the implementation of Regional Development Plans (€1623.9 million of EU Structural Funds (ERDF, CF, ESF+) and €378.1 million of national co-financing.) Fiscal equalisation mechanism between the state and municipalities: The Law on Methodology of Determination of Municipal Revenue, Articles 6-8. National regional development policy framework The National Progress Plan (NPP) – national document that identifies the main strategic goals and objectives to be achieved in all public policy areas. Strategic objective of the NPP #7: Sustainable and balanced development of the territory of Lithuania and reduction of regional exclusion. The basis for the Regional Development Programme (RDP). The Regional Development Programme for 2022–2030 (RDP) – based on strategic objectives, identified in the NPP, indicates the directions of implementation of objectives, for which Regional Development Councils and/or municipalities are responsible along to the competence established in regulations. 10 Regional Development Plans (RDPLs) – each region represented by Regional Development Council identifies social, economic and environmental problems and their causes within the region, determines the goals, objectives of regional development and indicators of monitoring, and plans the progress measures and preliminary funds. Urban policy framework Objectives of a national urban policy are integrated into the National Progress Plan (NPP) and the Regional Development Programme for 2022–2030 (RPP) Rural policy framework Objectives of a national rural policy are integrated into the National Progress Plan (NPP) and the Regional Development Programme for 2022–2030 (RPP) CAP Strategic Plan of Lithuania for 2023–2027 Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) European Structural Funds and national co-funding (27% of EUSF dedicated to Regional Development Programme for 2022–2030 and Regional Development Plans) The Regional Development Programme for 2022–2030 (RDP) The Regional Development Plans (RDPLs) Policy co-ordination tools at national level Ministry of Interior (national regional policy formation, organization, coordination and monitoring of its implementation) National Regional Development Council (it is the collegial advisory body for the Government and the Ministry of Interior in the area of national regional policy formulation and implementation. It consists from representatives of ministries, public authorities, the Association of Local Authorities in Lithuania, employers’ and trade unions’ organizations selected to the Tripartite Council, representatives of the Council of Non-Governmental Organisations and the National Council of Community Organizations, chairs of Regional Development Councils (RDCs)). Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) National Regional Development Council (It is empowered to discuss and consider the projects of planning documents being approved by the Government, including the Regional Development Programme, and regulations that may affect the regional development, and provide conclusions on these projects, also consider the progress of the implementation of these planning documents and, if necessary, submit proposals to the Government and the Ministry of Interior for the improvement of these documents, and consider other issues in the area of national regional policy formation and implementation) Policy co-ordination tools at regional level 10 Regional Development Councils (RDCs), one Council in each region, are legal entities established through an agreement between municipalities. RDCs are supra-municipal institutions. The body of RDC is the General meeting of participants; the governing bodies are the Panel (composed of the Mayors and members of the Municipal Councils) and the Administrative Director of RDC. Representing the region, their main competencies include: to plan and coordinate the implementation of the national regional policy in their respective region; to encourage social, economic development of the region, sustainable development of urbanised territories, decrease social and economic disparities within and across regions; and to encourage cooperation among municipalities in order to increase the efficiency of public services provision. The Administrative Director of RDC with small team (4-6 persons) acts as the secretariat of RDC. Also, RDC has an advisory Partner Group, that are engaged in delivering conclusions and opinions to the Panel regarding the projects of planning documents and other issues in the area of regional policy. 10 Regional Development Plans (RDPLs) prepared and approved by Regional Development Councils Sustainable Urban Development Strategies are prepared and implemented by urban municipalities in order to achieve the sustainable development of regional centres by solving social, economic, environmental and climate change challenges and in compliance with the principles of integrated approach. Agreements on Functional Zones among municipalities and Strategies for Sustainable Development of Functional Zones. Functional Zones are established and their strategies are prepared and implemented in order to increase the efficiency of the municipalities’ infrastructure and/or service network, to ensure the access to this infrastructure and services for all functional zone residents, and to create conditions for the joint actions of several municipalities and the implementation of joint investment projects. Evaluation and monitoring tools Ministry of Interior (monitoring the progress of implementation of the Regional Development Programme for 2022-2030, consulting RDCs on the consistency of RDPLs with the aim and objectives of the national regional policy, other issues of RDPLs preparation, implementation and monitoring in the area of Ministry competence) Central Project Management Agency (methodical guidance for strategic planning, project management, monitoring, supervision, evaluation, financial management, progress reporting) System of indicators and targets along to the 2021-2030 National Progress Plan (NPP), the Regional Development Programme for 2022–2030 and 10 Regional Development Plans Future orientations of regional policy Future orientations of regional policy focus on following issues: Decrease interregional social and economic disparities by strengthening regions in most vulnerable areas in terms of target groups, public services, transportation and environmental issues. Decrease intraregional social and economic disparities by implementing the measures within the region towards economic, social or environmental issues in particular territory."
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- "text": "Regional inequality trends\nLithuania experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2007. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.177 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.064 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.804. For reference, the same value for OECD was 1.325. This gap increased by 0.414 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.804 in 2020 and increased by 0.414 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Lithuania, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 89%, 12 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Lithuania, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nLithuania experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2007. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.177 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.064 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.804. For reference, the same value for OECD was 1.325. This gap increased by 0.414 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.804 in 2020 and increased by 0.414 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Lithuania, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 89%, 12 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Lithuania, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n126,014,024 (2020), 1 964 375 km2 (2018)\n\n\n\n\nAdministrative structure\nFederal\n\n\nRegional or state-level governments\n32 States\n\n\nIntermediate-level governments\nN/A\n\n\nMunicipal-level governments\n2469 municipalities\n\n\nShare of subnational government in total expenditure/revenues (2021)\n40.0% of total expenditure\n50.6% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey challenges\nMexico has undergone a process of important territorial changes in recent decades. However, housing policies in recent years, constitutional modifications on agrarian property and the lack of rigorous application of planning and land use planning instruments, among other factors, have accelerated urban expansion over agricultural and natural areas (ENOT, 2020-2040).\nThis has brought socio-territorial inequalities that are manifested in the National Territorial System in which large cities concentrate services, jobs and infrastructure consuming resources indiscriminately, while their extensive and diffuse peripheries, as well as dispersed rural localities and indigenous communities, present serious problems of access to basic services and forms of subsistence in their environment. In addition, they do not have a mobility system that connects them, nor decent and safe housing, exacerbating the backwardness in which they live (PNOTDU 2021 - 2024).\nIn this sense, land-use planning requires effective and updated planning strategies and instruments in co-responsibility with the agencies involved in land-use planning, which allow for the articulation of ecological planning with the planning of human settlements and productive activities.\n\n\nObjectives of regional policy\nSEDATU, as head of the land use and urban development sector, in accordance with the policy principles established in the General Law on Human Settlements, Territorial Planning and Urban Development (LGAHOTDU by its acronym in Spanish), developed the National Land Management Strategy 2020-2024 (ENOT by its acronym in Spanish), which sets out the direction in which Mexico should move over the next twenty years to achieve a more sustainable scenario in the use and exploitation of land resources.\nThe ENOT 2020-2040 identifies 6 macro-regions and 20 Urban-Rural Systems (SUR by its acronym in Spanish) that functionally structure the country to deepen the existing functional relationships between states, cities, metropolitan areas and rural localities.\nFor its part, the Territorial Planning and Urban Development National Programme 2021-2024 (PNOTDU by its acronym in Spanish) sets out six intrinsically related objectives with which it intends to move towards new territories:\n\nPromote a fair, balanced and sustainable territorial development model for the wellbeing of the population and its environment.\nPromote integrated development in Urban-Rural Systems and Metropolitan Areas.\nTransition to an urban development model oriented towards\nSustainable, orderly, equitable, just, and economically viable cities that reduce socio-spatial inequalities in human settlements.\nStrengthen the organizational, productive and sustainable development capacities of the agrarian sector, rural and indigenous and Afro-Mexican population and communities in the territory, with cultural relevance.\nPromote the integral habitat of the population in the adequate housing policy.\nStrengthen sustainability and adaptive capacities in the territory and its inhabitants\n\n\n\nLegal/institutional framework for regional policy\nThe General Law on Human Settlements, Territorial Planning and Urban Development (LGAHOTDU, Article 8 section VII) establishes that the Ministry of Agrarian, Territorial and Urban Development (SEDATU) is responsible for planning, designing, promoting and evaluating financing mechanisms for regional, urban and rural development, with the participation of the Federal Public Administration and other levels of government, as well as to promote and execute the construction of infrastructure and equipment works for regional, urban and rural development to promote access for all to the services, benefits and prosperity offered by cities..\nLikewise, the National Development Plan (2019-2024) recognizes that under the principle of Leave no one behind, leave no one out and Economy for Well-being, regional projects will be designed to address specific needs, based on the following priority projects:\n\nMayan Train: infrastructure, socio-economic development and tourism project aimed at increasing the economic flow of tourism in the Yucatan Peninsula.\nProgramme for the Development of the Isthmus of Tehuantepec: its objective is to promote the growth of the regional economy with full respect for the history, culture and traditions of the Oaxacan and Veracruz Isthmus.\nNorthern Border Free Zone Programme: applied in the 43 border municipalities with the United States, offering development benefits (tax reductions, minimum wage increases, etc.).\n\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\n\n\n\nNational regional development policy framework\n\nNational Development Plan 2019 – 2024\nSectoral Programme for Agrarian, Territorial and Urban Development 2020 - 2024 (PSEDATU)\nNational Land Management Strategy 2020 - 2040 (ENOT)\nTerritorial Planning and Urban Development National Programme 2021-2024 (PNOTDU)\n\n\n\nUrban policy framework\n\nNational Development Plan 2019 – 2024\nSectoral Programme for Agrarian, Territorial and Urban Development 2020 - 2024 (PSEDATU)\nNational Land Management Strategy 2020 - 2040 (ENOT)\nTerritorial Planning and Urban Development National Programme 2021-2024 (PNOTDU)\n\n\n\nRural policy framework\n\nNational Development Plan 2019 – 2024\nProgrammes deriving from the agriculture and rural development sector under the Ministry of Agriculture and Rural Development (SADER by its acronym in Spanish).\n\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nSome of the instruments created by SEDATU to address regional development linked to priority projects of the Government of Mexico are the following:\n\nRegionalisation of the National Land Management Strategy 2020- 2040 (ENOT): The ENOT is a 20-year planning instrument that identifies 6 macro-regions and 20 Urban-Rural Systems (SUR) that functionally structure the country. The identification and definition of macro-regions and Urban-Rural Systems (SUR) are a way of deepening the functional relationships between states, cities, metropolitan areas, and rural localities. The SURs are basic spatial units that group together non-urbanized areas, urban centers and rural settlements that are functionally linked.\nTerritorial Planning Programme for the Isthmus of Tehuantepec Region (POT-RIT): Its objective is to establish an instrument that is the guiding axis that configures the territorial development of the Isthmus of Tehuantepec region, through a model of sustainable, multi-scale, fair territorial planning with a systemic approach in the short, medium, and long term, which favors urbanization processes and the use of forms of occupation compatible with the territory, and the rational use and exploitation of its resources.\nLand Management Programme for the South-South-East Region: The main objective of the Programme, framed within the National Land Management Strategy (ENOT), is to generate a land management model and public policies in the short, medium, and long term, aimed at guiding sustainable, equitable and inclusive development and occupation in the territory, being the guiding axis for harmonizing state, metropolitan and municipal or local land planning instruments, linked to the National Land Policy.\nProgramme for the Development of the Isthmus of Tehuantepec 2020-2024 (PDIT): Its objective is to promote the growth of the regional economy with full respect for the history, culture, and traditions of the Oaxacan and Veracruz Isthmus to generate the conditions for an inclusive economy that promotes the wellbeing of the population and guarantees a fair distribution of benefits.\n\n\n\nPolicy co-ordination at national level\nNational Council for Territorial Planning and Urban Development (CNOTDU): Consultative body on land-use planning and urban development made up of more than 40 agencies and entities of the Federal Public Administration, including eight State Secretariats: representatives of states and municipalities, the Chamber of Deputies, the Senate of the Republic, as well as society organizations. It seeks to be a tool for communication and coordination between the actors involved, as well as a mechanism for democratic planning and management where the active participation of the population is encouraged. Likewise, the Council is the consultative body whose attributions include the monitoring and evaluation of national land-use planning policies. Thus, the monitoring and evaluation of the ENOT will be carried out within this collegiate body\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\nNational Metropolitan Network (RENAMET): Its objective is to strengthen the metropolitan network through the exchange of coordination and territorial planning experiences within the framework of the binational agenda. It functions as a mechanism to promote the transfer of knowledge, methodologies, best practices, professionalization and information exchange, as well as the development of strategic projects within this territorial scale.\nState councils for territorial planning and urban development: The State Councils have the objective of contributing to improve public policies on urban and territorial development to build a national strategy with a long-term vision. These councils are a mandate derived from the LGAHOTDU, where there must be a national council and state and municipal replicas in conurbations and metropolitan regions of the country, to determine the policies and planning of urban and territorial development, to have different visions of the same problem and thus enrich the management of land use planning.\nMetropolitan Development Advisory Councils: The LGAHOTDU (Article 19) establishes that, in order to ensure consultation, opinion and deliberation of land use and urban development and metropolitan development planning policies, the federal entities, and municipalities, within the scope of their respective competences, will form the following auxiliary bodies of citizen participation and plural composition:\n\nThe State Councils for Territorial Planning and Urban Development.\nMetropolitan and conurbation commissions, and\nMunicipal councils for urban development and housing if necessary.\n\nMetropolitan planning commissions: They include the participation of different actors from the three levels of government, the private sector, civil society, and academia. Currently, 58 out of 74 Commissions are installed and meet according to the nature of each Metropolitan Zone. Since 2020, they have been chaired by Sedatu and their objective is to reach a consensus on decision-making with respect to land use planning and urban development.\n\n\n\nPolicy co-ordination at regional level\n\n\n\nEvaluation and monitoring\nENOT Evaluation and Monitoring Working Group: The monitoring and evaluation of the ENOT 2020-2040 is coordinated by Sedatu with the participation of a Working Group for the Monitoring and Evaluation of the ENOT (WG) that is integrated by representatives of the different social, private and public sectors, and may request the necessary information from the State Councils for Territorial Planning and Urban Development (CEOTDU) to monitor the Strategy, analyze the information and prepare inputs for the presentation of progress, issue opinions and recommendations, and prepare reports to the National Council and State Councils for Territorial Planning and Urban Development.\n\n\nFuture orientations of regional policy\nENOT Vision 2020-2040: In twenty years’ time, ENOT should:\n\nIdentify the Urban-Rural Systems and regionalization that functionally structure the country, as well as guide the delimitation and characterization of strategic metropolitan areas to boost economic development and reduce regional disparities.\nTo propose measures for the sustainable development of the country's regions in terms of their natural resources, their productive activities and the balance between human settlements and their environmental conditions.\nPropose guidelines for the provision of infrastructure, equipment and facilities that are essential for the development of the regions and the country."
+ "text": "Overview\n\n\n\n Population and territory 126,014,024 (2020), 1 964 375 km2 (2018) Administrative structure Federal Regional or state-level governments 32 States Intermediate-level governments N/A Municipal-level governments 2469 municipalities Share of subnational government in total expenditure/revenues (2021) 40.0% of total expenditure 50.6% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key challenges Mexico has undergone a process of important territorial changes in recent decades. However, housing policies in recent years, constitutional modifications on agrarian property and the lack of rigorous application of planning and land use planning instruments, among other factors, have accelerated urban expansion over agricultural and natural areas (ENOT, 2020-2040). This has brought socio-territorial inequalities that are manifested in the National Territorial System in which large cities concentrate services, jobs and infrastructure consuming resources indiscriminately, while their extensive and diffuse peripheries, as well as dispersed rural localities and indigenous communities, present serious problems of access to basic services and forms of subsistence in their environment. In addition, they do not have a mobility system that connects them, nor decent and safe housing, exacerbating the backwardness in which they live (PNOTDU 2021 - 2024). In this sense, land-use planning requires effective and updated planning strategies and instruments in co-responsibility with the agencies involved in land-use planning, which allow for the articulation of ecological planning with the planning of human settlements and productive activities. Objectives of regional policy SEDATU, as head of the land use and urban development sector, in accordance with the policy principles established in the General Law on Human Settlements, Territorial Planning and Urban Development (LGAHOTDU by its acronym in Spanish), developed the National Land Management Strategy 2020-2024 (ENOT by its acronym in Spanish), which sets out the direction in which Mexico should move over the next twenty years to achieve a more sustainable scenario in the use and exploitation of land resources. The ENOT 2020-2040 identifies 6 macro-regions and 20 Urban-Rural Systems (SUR by its acronym in Spanish) that functionally structure the country to deepen the existing functional relationships between states, cities, metropolitan areas and rural localities. For its part, the Territorial Planning and Urban Development National Programme 2021-2024 (PNOTDU by its acronym in Spanish) sets out six intrinsically related objectives with which it intends to move towards new territories: Promote a fair, balanced and sustainable territorial development model for the wellbeing of the population and its environment. Promote integrated development in Urban-Rural Systems and Metropolitan Areas. Transition to an urban development model oriented towards Sustainable, orderly, equitable, just, and economically viable cities that reduce socio-spatial inequalities in human settlements. Strengthen the organizational, productive and sustainable development capacities of the agrarian sector, rural and indigenous and Afro-Mexican population and communities in the territory, with cultural relevance. Promote the integral habitat of the population in the adequate housing policy. Strengthen sustainability and adaptive capacities in the territory and its inhabitants Legal/institutional framework for regional policy The General Law on Human Settlements, Territorial Planning and Urban Development (LGAHOTDU, Article 8 section VII) establishes that the Ministry of Agrarian, Territorial and Urban Development (SEDATU) is responsible for planning, designing, promoting and evaluating financing mechanisms for regional, urban and rural development, with the participation of the Federal Public Administration and other levels of government, as well as to promote and execute the construction of infrastructure and equipment works for regional, urban and rural development to promote access for all to the services, benefits and prosperity offered by cities.. Likewise, the National Development Plan (2019-2024) recognizes that under the principle of Leave no one behind, leave no one out and Economy for Well-being, regional projects will be designed to address specific needs, based on the following priority projects: Mayan Train: infrastructure, socio-economic development and tourism project aimed at increasing the economic flow of tourism in the Yucatan Peninsula. Programme for the Development of the Isthmus of Tehuantepec: its objective is to promote the growth of the regional economy with full respect for the history, culture and traditions of the Oaxacan and Veracruz Isthmus. Northern Border Free Zone Programme: applied in the 43 border municipalities with the United States, offering development benefits (tax reductions, minimum wage increases, etc.). Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) National regional development policy framework National Development Plan 2019 – 2024 Sectoral Programme for Agrarian, Territorial and Urban Development 2020 - 2024 (PSEDATU) National Land Management Strategy 2020 - 2040 (ENOT) Territorial Planning and Urban Development National Programme 2021-2024 (PNOTDU) Urban policy framework National Development Plan 2019 – 2024 Sectoral Programme for Agrarian, Territorial and Urban Development 2020 - 2024 (PSEDATU) National Land Management Strategy 2020 - 2040 (ENOT) Territorial Planning and Urban Development National Programme 2021-2024 (PNOTDU) Rural policy framework National Development Plan 2019 – 2024 Programmes deriving from the agriculture and rural development sector under the Ministry of Agriculture and Rural Development (SADER by its acronym in Spanish). Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Some of the instruments created by SEDATU to address regional development linked to priority projects of the Government of Mexico are the following: Regionalisation of the National Land Management Strategy 2020- 2040 (ENOT): The ENOT is a 20-year planning instrument that identifies 6 macro-regions and 20 Urban-Rural Systems (SUR) that functionally structure the country. The identification and definition of macro-regions and Urban-Rural Systems (SUR) are a way of deepening the functional relationships between states, cities, metropolitan areas, and rural localities. The SURs are basic spatial units that group together non-urbanized areas, urban centers and rural settlements that are functionally linked. Territorial Planning Programme for the Isthmus of Tehuantepec Region (POT-RIT): Its objective is to establish an instrument that is the guiding axis that configures the territorial development of the Isthmus of Tehuantepec region, through a model of sustainable, multi-scale, fair territorial planning with a systemic approach in the short, medium, and long term, which favors urbanization processes and the use of forms of occupation compatible with the territory, and the rational use and exploitation of its resources. Land Management Programme for the South-South-East Region: The main objective of the Programme, framed within the National Land Management Strategy (ENOT), is to generate a land management model and public policies in the short, medium, and long term, aimed at guiding sustainable, equitable and inclusive development and occupation in the territory, being the guiding axis for harmonizing state, metropolitan and municipal or local land planning instruments, linked to the National Land Policy. Programme for the Development of the Isthmus of Tehuantepec 2020-2024 (PDIT): Its objective is to promote the growth of the regional economy with full respect for the history, culture, and traditions of the Oaxacan and Veracruz Isthmus to generate the conditions for an inclusive economy that promotes the wellbeing of the population and guarantees a fair distribution of benefits. Policy co-ordination at national level National Council for Territorial Planning and Urban Development (CNOTDU): Consultative body on land-use planning and urban development made up of more than 40 agencies and entities of the Federal Public Administration, including eight State Secretariats: representatives of states and municipalities, the Chamber of Deputies, the Senate of the Republic, as well as society organizations. It seeks to be a tool for communication and coordination between the actors involved, as well as a mechanism for democratic planning and management where the active participation of the population is encouraged. Likewise, the Council is the consultative body whose attributions include the monitoring and evaluation of national land-use planning policies. Thus, the monitoring and evaluation of the ENOT will be carried out within this collegiate body Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) National Metropolitan Network (RENAMET): Its objective is to strengthen the metropolitan network through the exchange of coordination and territorial planning experiences within the framework of the binational agenda. It functions as a mechanism to promote the transfer of knowledge, methodologies, best practices, professionalization and information exchange, as well as the development of strategic projects within this territorial scale. State councils for territorial planning and urban development: The State Councils have the objective of contributing to improve public policies on urban and territorial development to build a national strategy with a long-term vision. These councils are a mandate derived from the LGAHOTDU, where there must be a national council and state and municipal replicas in conurbations and metropolitan regions of the country, to determine the policies and planning of urban and territorial development, to have different visions of the same problem and thus enrich the management of land use planning. Metropolitan Development Advisory Councils: The LGAHOTDU (Article 19) establishes that, in order to ensure consultation, opinion and deliberation of land use and urban development and metropolitan development planning policies, the federal entities, and municipalities, within the scope of their respective competences, will form the following auxiliary bodies of citizen participation and plural composition: The State Councils for Territorial Planning and Urban Development. Metropolitan and conurbation commissions, and Municipal councils for urban development and housing if necessary. Metropolitan planning commissions: They include the participation of different actors from the three levels of government, the private sector, civil society, and academia. Currently, 58 out of 74 Commissions are installed and meet according to the nature of each Metropolitan Zone. Since 2020, they have been chaired by Sedatu and their objective is to reach a consensus on decision-making with respect to land use planning and urban development. Policy co-ordination at regional level Evaluation and monitoring ENOT Evaluation and Monitoring Working Group: The monitoring and evaluation of the ENOT 2020-2040 is coordinated by Sedatu with the participation of a Working Group for the Monitoring and Evaluation of the ENOT (WG) that is integrated by representatives of the different social, private and public sectors, and may request the necessary information from the State Councils for Territorial Planning and Urban Development (CEOTDU) to monitor the Strategy, analyze the information and prepare inputs for the presentation of progress, issue opinions and recommendations, and prepare reports to the National Council and State Councils for Territorial Planning and Urban Development. Future orientations of regional policy ENOT Vision 2020-2040: In twenty years’ time, ENOT should: Identify the Urban-Rural Systems and regionalization that functionally structure the country, as well as guide the delimitation and characterization of strategic metropolitan areas to boost economic development and reduce regional disparities. To propose measures for the sustainable development of the country's regions in terms of their natural resources, their productive activities and the balance between human settlements and their environmental conditions. Propose guidelines for the provision of infrastructure, equipment and facilities that are essential for the development of the regions and the country."
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- "text": "Regional inequality trends\nMexico experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \nIn Mexico, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2005 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 3%, while it increased by 6% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Mexico, between 2005 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nMexico experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Mexico, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2005 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 3%, while it increased by 6% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Mexico, between 2005 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n17 813 121 (November 2022) - 41,543 km2\n\n\n\n\nAdministrative structure \nUnitary\n\n\nRegional or state-level governments \n12\n\n\nIntermediate-level governments \nN/A\n\n\nMunicipal-level governments \n342 (January 2023)\n\n\nShare of subnational government in total expenditure/revenues (2021)\n29.0% of total expenditure\n30.9% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nSee further details below\n\n\nObjectives of regional policy\nSee further details below\n\n\nLegal/institutional framework for regional policy\nNo explicit regional development framework.\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nN/A\n\n\nNational regional development policy framework\nThere is no explicit regional development framework. The Netherlands currently does not have an explicit regional policy but applies a regional focus to several policy domains. The focus on regional strengths and attention to regional differences has been followed up and strengthened by the new government formed in October 2017. The new Regional Budget, for example, is a financial instrument that allows co-operation and collaboration between the national government, regional governments, the business community, academia and civil society on addressing specific regional challenges. In addition, the Ministry of Interior and Kingdom Relations co-ordinates the National Urban Agenda (Agenda Stad), which includes measures to boost economic growth, quality of life and innovation in Dutch cities and supports the\ncreation of city deals.\n\n\nUrban policy framework\nThere is no explicit national urban policy framework.\n\n\nRural policy framework\nRural Development Program\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nSee further details below\n\n\nPolicy co-ordination tools at national level\nSee further details below\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\nSee further details below\n\n\nPolicy co-ordination tools at regional level\nSee further details below\n\n\nEvaluation and monitoring tools\nN/A\n\n\nFuture orientations of regional policy\nSee further details below"
+ "text": "Overview\n\n\n\n Population and territory 17 813 121 (November 2022) - 41,543 km2 Administrative structure Unitary Regional or state-level governments 12 Intermediate-level governments N/A Municipal-level governments 342 (January 2023) Share of subnational government in total expenditure/revenues (2021) 29.0% of total expenditure 30.9% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges See further details below Objectives of regional policy See further details below Legal/institutional framework for regional policy No explicit regional development framework. Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) N/A National regional development policy framework There is no explicit regional development framework. The Netherlands currently does not have an explicit regional policy but applies a regional focus to several policy domains. The focus on regional strengths and attention to regional differences has been followed up and strengthened by the new government formed in October 2017. The new Regional Budget, for example, is a financial instrument that allows co-operation and collaboration between the national government, regional governments, the business community, academia and civil society on addressing specific regional challenges. In addition, the Ministry of Interior and Kingdom Relations co-ordinates the National Urban Agenda (Agenda Stad), which includes measures to boost economic growth, quality of life and innovation in Dutch cities and supports the creation of city deals. Urban policy framework There is no explicit national urban policy framework. Rural policy framework Rural Development Program Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) See further details below Policy co-ordination tools at national level See further details below Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) See further details below Policy co-ordination tools at regional level See further details below Evaluation and monitoring tools N/A Future orientations of regional policy See further details below"
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- "text": "Regional inequality trends\nThe Netherlands experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2013. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.33. For reference, the same value for OECD was 1.475. This gap increased by 0.044 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.238. For reference, the same value for OECD was 1.325. This gap decreased by 0.061 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn the Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nThe Netherlands experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2013. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.33. For reference, the same value for OECD was 1.475. This gap increased by 0.044 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.238. For reference, the same value for OECD was 1.325. This gap decreased by 0.061 percentage points since 2000.\nThere is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn the Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\nPopulation: 5,127,400 (as of 30 September 2022) Territory: 268,021 km²\n\n\n\n\nAdministrative structure \nUnitary parliamentary democracy under a constitutional monarchy\n\n\nRegional or state-level governments \n11 regional councils\n\n\nIntermediate-level governments \n-\n\n\nMunicipal-level governments \n67 territorial authorities\n\n\nShare of subnational government in total expenditure/revenues (2021)\n10.7% of total expenditure\n10.8% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\n\nIncreasing disparities in wealth and opportunity, skill shortages and pockets of unemployment.\nProductivity challenges, with poor resource efficiency and slow growth.\nTight labour market.\nSocial infrastructure, particularly housing.\nAdvancing technologies and climate change are affecting traditional jobs.\n\n\n\nObjectives of regional policy\nThe Government’s objective is supporting regional economies to become more productive, resilient, inclusive, sustainable and Māori-enabling (PRISM).\nThe Regional Strategic Partnership Fund (RSPF) is a $200 million fund which is a strategic investment approach and coordinated regional economic development work programme that supports regions to work towards achieving their economic potential. The RSPF is the Government’s main lever for regional economic development and aims to improve the economic prospects, and through this the living standards, of New Zealanders by delivering local approaches tailored to the particular needs of individual regions.\n\n\nLegal/institutional framework for regional policy\n\nTe Tiriti o Waitangi/Treaty of Waitangi\nResource Management Act 1991 (reforms currently underway)\nLocal Government Act 2002\nPublic Service Act 2020\nNew Zealand Bill of Rights Act 1990\nHuman Rights Act 1993.\n\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nSince 2018, the Government has allocated $4.5 billion towards a range of regional economic development funds and other initiatives managed and administered by Kānoa – Regional Economic Development & Investment Unit.\nThere are also a range of funds and initiatives throughout Government. Budget 2022 provided funding for a number of initiatives for the 2022-2023 financial year including:\n\nToitu Te Whenua Regional Housing Improvement Programme - $3.6 million\nPort Sector Opportunities to Support Decarbonisation, Resilience, and Regional Development - $3.7 million\nImproving rural connectivity - $15 million\nDolomite Point Redevelopment Project $2.229 million\nEquitable Transitions Programme - $4.523 million\nManaging the Regional Strategic Partnership Fund - $13 million\nThe Regional Strategic Partnership Fund Operational Costs - $6.945 million\n\n\n\nNational regional development policy framework\nThe PRISM Regional Economies Framework supports regional economies to be more productive, resilient, inclusive, sustainable and Māori – enabling. The framework was developed to help deliver local approaches tailored to regions’ particular needs and advantages. Achieving more PRISM communities is a long-term vision, which takes time and requires funding and other interventions from across government, regions, local communities and businesses.\nThe Regional Systems Leadership Framework allocates the role of Regional Public Service Commissioner to a senior public servant in each region. Their mandate is to coordinate and align central government, coordinate with officials to resolve issues and escalate barriers to chief executives. It aims to embed new ways of working to better align how agencies invest and deliver services.\n\n\nUrban policy framework\nThe National Policy Statement on Urban Development 2020 aims to ensure that New Zealand’s towns and cities are well-functioning urban environments that meet the changing needs of our diverse communities.\n\n\nRural policy framework\nRural Proofing Guide for policy development and service delivery planning 2018.\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nFunds:\n\nRegional Strategic Partnership Fund\nThe Provincial Growth Fund\nThe COVID-19 Response and Recovery Fund\nInfrastructure Reference Group Fund\nThe Strategic Tourism Assets Protection Programme\nNZ Upgrade Programme: Regional Investment Opportunities\nCOVID-19 Worker Redeployment Initiative\nHe Poutama Rangatahi\nThe Māori Trades and Training Fund\nThe Sector Workforce Engagement Programme.\n\nPlans:\n\nIndustry Transformation Plans\nRegional Workforce Plans\nRegional Land Transport Plans\nRegional Economic Development Partnership groups’ regional priorities.\n\nThe Government’s Economic Plan supports New Zealand to become a high-wage, low-emissions economy that provides economic security in good times and bad. There are five focus areas:\n\nunleashing business potential;\nstrengthening international connections;\nincreasing capabilities and opportunities;\nsupporting Māori and Pacific aspirations; and\nstrengthening our foundations.\n\n\n\nPolicy co-ordination tools at national level\n\nRegional Public Service Commissioners\n\nGovernment Cabinet committees:\n\nCabinet Economic Development Committee\nRegional Economic Development Ministers group\nMāori Economic Development Ministers group\n\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n\nUrban Growth Agenda\nWaka Kotahi National Land Transport Programme 2021-2024\nNational Bill Environment Act\n\n\n\nPolicy co-ordination tools at regional level\n\nRegional Economic Development Senior Officials Group\nRegional Economic Development Partnership Groups\nRegional Skills Leadership Groups\n\n\n\nEvaluation and monitoring tools\nImpact Management Framework: Measures the impact of the Regional Strategic Partnership Fund.\nEvaluation of the Provincial Growth Fund.\nCabinet’s Impact Analysis Requirements support and inform the government’s decisions on regulatory proposals. They are both a process and an analytical framework that encourages a systematic and evidence-informed approach to policy development. The requirements incorporate the Government Expectations for Good Regulatory Practice. In particular, the requirements focus on the expectation that agencies provide robust analysis and advice to Ministers before decisions are taken on regulatory change.\nCabinet papers to be considered by government have a section titled ‘Population Implications’. The section should be used to summarise the impacts that proposals are likely to have on population groups, as appropriate to the issue, and any actions that will be taken to address negative impacts.\nCabinet papers should also have a statement on whether the proposal is in any way inconsistent with the New Zealand Bill of Rights Act 1990 and the Human Rights Act 1993.\n\n\nFuture orientations of regional policy\nThe Government aims to continue to build more productive, resilient, inclusive, sustainable and Māori-enabling (PRISM) regional economies by delivering local approaches tailored to a region's particular needs and advantages."
+ "text": "Overview\n\n\n\n Population and territory Population: 5,127,400 (as of 30 September 2022) Territory: 268,021 km² Administrative structure Unitary parliamentary democracy under a constitutional monarchy Regional or state-level governments 11 regional councils Intermediate-level governments - Municipal-level governments 67 territorial authorities Share of subnational government in total expenditure/revenues (2021) 10.7% of total expenditure 10.8% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Increasing disparities in wealth and opportunity, skill shortages and pockets of unemployment. Productivity challenges, with poor resource efficiency and slow growth. Tight labour market. Social infrastructure, particularly housing. Advancing technologies and climate change are affecting traditional jobs. Objectives of regional policy The Government’s objective is supporting regional economies to become more productive, resilient, inclusive, sustainable and Māori-enabling (PRISM). The Regional Strategic Partnership Fund (RSPF) is a $200 million fund which is a strategic investment approach and coordinated regional economic development work programme that supports regions to work towards achieving their economic potential. The RSPF is the Government’s main lever for regional economic development and aims to improve the economic prospects, and through this the living standards, of New Zealanders by delivering local approaches tailored to the particular needs of individual regions. Legal/institutional framework for regional policy Te Tiriti o Waitangi/Treaty of Waitangi Resource Management Act 1991 (reforms currently underway) Local Government Act 2002 Public Service Act 2020 New Zealand Bill of Rights Act 1990 Human Rights Act 1993. Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) Since 2018, the Government has allocated $4.5 billion towards a range of regional economic development funds and other initiatives managed and administered by Kānoa – Regional Economic Development & Investment Unit. There are also a range of funds and initiatives throughout Government. Budget 2022 provided funding for a number of initiatives for the 2022-2023 financial year including: Toitu Te Whenua Regional Housing Improvement Programme - $3.6 million Port Sector Opportunities to Support Decarbonisation, Resilience, and Regional Development - $3.7 million Improving rural connectivity - $15 million Dolomite Point Redevelopment Project $2.229 million Equitable Transitions Programme - $4.523 million Managing the Regional Strategic Partnership Fund - $13 million The Regional Strategic Partnership Fund Operational Costs - $6.945 million National regional development policy framework The PRISM Regional Economies Framework supports regional economies to be more productive, resilient, inclusive, sustainable and Māori – enabling. The framework was developed to help deliver local approaches tailored to regions’ particular needs and advantages. Achieving more PRISM communities is a long-term vision, which takes time and requires funding and other interventions from across government, regions, local communities and businesses. The Regional Systems Leadership Framework allocates the role of Regional Public Service Commissioner to a senior public servant in each region. Their mandate is to coordinate and align central government, coordinate with officials to resolve issues and escalate barriers to chief executives. It aims to embed new ways of working to better align how agencies invest and deliver services. Urban policy framework The National Policy Statement on Urban Development 2020 aims to ensure that New Zealand’s towns and cities are well-functioning urban environments that meet the changing needs of our diverse communities. Rural policy framework Rural Proofing Guide for policy development and service delivery planning 2018. Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Funds: Regional Strategic Partnership Fund The Provincial Growth Fund The COVID-19 Response and Recovery Fund Infrastructure Reference Group Fund The Strategic Tourism Assets Protection Programme NZ Upgrade Programme: Regional Investment Opportunities COVID-19 Worker Redeployment Initiative He Poutama Rangatahi The Māori Trades and Training Fund The Sector Workforce Engagement Programme. Plans: Industry Transformation Plans Regional Workforce Plans Regional Land Transport Plans Regional Economic Development Partnership groups’ regional priorities. The Government’s Economic Plan supports New Zealand to become a high-wage, low-emissions economy that provides economic security in good times and bad. There are five focus areas: unleashing business potential; strengthening international connections; increasing capabilities and opportunities; supporting Māori and Pacific aspirations; and strengthening our foundations. Policy co-ordination tools at national level Regional Public Service Commissioners Government Cabinet committees: Cabinet Economic Development Committee Regional Economic Development Ministers group Māori Economic Development Ministers group Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) Urban Growth Agenda Waka Kotahi National Land Transport Programme 2021-2024 National Bill Environment Act Policy co-ordination tools at regional level Regional Economic Development Senior Officials Group Regional Economic Development Partnership Groups Regional Skills Leadership Groups Evaluation and monitoring tools Impact Management Framework: Measures the impact of the Regional Strategic Partnership Fund. Evaluation of the Provincial Growth Fund. Cabinet’s Impact Analysis Requirements support and inform the government’s decisions on regulatory proposals. They are both a process and an analytical framework that encourages a systematic and evidence-informed approach to policy development. The requirements incorporate the Government Expectations for Good Regulatory Practice. In particular, the requirements focus on the expectation that agencies provide robust analysis and advice to Ministers before decisions are taken on regulatory change. Cabinet papers to be considered by government have a section titled ‘Population Implications’. The section should be used to summarise the impacts that proposals are likely to have on population groups, as appropriate to the issue, and any actions that will be taken to address negative impacts. Cabinet papers should also have a statement on whether the proposal is in any way inconsistent with the New Zealand Bill of Rights Act 1990 and the Human Rights Act 1993. Future orientations of regional policy The Government aims to continue to build more productive, resilient, inclusive, sustainable and Māori-enabling (PRISM) regional economies by delivering local approaches tailored to a region's particular needs and advantages."
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- "text": "Regional inequality trends\nNew Zealand experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.072 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.04 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.231. For reference, the same value for OECD was 1.325. This gap increased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.231 in 2020 and increased by 0.016 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn New Zealand, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 10 percentage points less than in the lower half of regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nNew Zealand experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.072 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.04 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.231. For reference, the same value for OECD was 1.325. This gap increased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.231 in 2020 and increased by 0.016 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn New Zealand, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 10 percentage points less than in the lower half of regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"title": "Norway",
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- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n5 425 270 (as of January 1, 2022) 323 810 km2\n\n\n\n\nAdministrative structure \nUnitary country\n\n\nRegional or state-level governments \n11 Regions (fylker)\n\n\nIntermediate-level governments \n\n\n\nMunicipal-level governments \n356 Municipalities (kommuner)\n\n\nShare of subnational government in total expenditure/revenues (2021)\n33.0% of total expenditure\n27.7% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nNorwegian regional issues are characterized by targeting areas with very low population density and limited accessibility to jobs and services. Regional disparities in income and unemployment are modest but labour and skills shortages and the age ratio in peripheral areas (‘distriktene’) have become pressing issues.\n\n\nObjectives of regional policy\nThe goal of regional and rural (‘distrikt’) policy is ‘that people can live a good life throughout Norway, all local communities have room for development and economic growth, and increase in the population in rural municipalities. Ensure that people have access to work, housing and good services nearby. Facilitate safe, sustainable and vibrant local communities throughout the country through decentralized solutions.\n\n\nLegal/institutional framework for regional policy\nAt the national level, the regional policy lead is the Department for Regional Development. Regional Policy Department has a coordinating role, working to ensure the priorities and measures of all sectors support regional development.\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nBudget allocation to rural (‘distrikt’) and regional policy comprises:\n- ‘narrow’ or targeted policies financed from the KDD with a budget in 2023 of NOK 1.3 billion\n- ‘broad’ measures financed from other budget lines, including other ministries (c. NOK 57 billion in 2023)\nIn addition, there are large income equalization mechanisms between regions and municipalities\n\n\nNational regional development policy framework\nThe 2023 White Paper -– Meld. St. 27 (2022-23) ‘A good life throughout Norway - district policy for the future”\n\n\nUrban policy framework\nThe 2017 White Paper ‘Urban sustainability and rural strength’ and the 2023 White Paper Meld. St. 28 (2022–2023) Good urban communities with small inequalities\n\n\nRural policy framework\nThe 2023 White Paper – Meld. St. 27 (2022-23) ‘A good life throughout Norway - district policy for the future\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nFunding for ‘narrow’ regional policy (NOK 1.3 billion) to (i) Growing businesses, value creation and attractive labour markets in regions and districts; (ii) Regional development across national borders and in the High North; and (iii) Capacity building and basic services in the peripheral areas (‘distriktene’).\nFunding for ‘broad’ regional policy spend, amount to some NOK 57 billion comprises of\nA. Measures and arrangements that are based on rural (‘distrikt’) policy goals or that favour peripheral areas (‘distriktene’) beyond simple compensation to achieve equal opportunities\n- the regionally-differentiated social security concession\n- a package of (mainly tax) measures for the northern Troms and Finnmark ‘Action Zone’\n- special measures for northern Norway (including a VAT exemption on energy from renewables) and grants for municipalities in southern Norway?\nB. Measures which aim to equalise or compensate between geographical areas and that are important for economic growth, employment or housing in rural areas. Category B comprises a range of sectoral measures including subsidies for land development, infrastructure, agriculture, cultural heritage and museums.\nArea-based urban initiatives: The Government has cooperation agreements with Urban municipalities with major challenges in living conditions in parts of the Citiies.\nPolicy guidelines on location of public sector jobs and public services (state)\n\n\nPolicy co-ordination tools at national level\nMinistry of Local Government and Regional Development (KDD) Coordination between KDD and other ministries\nState Secretary committee on high north policy\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\nNational expectations regarding regional and municipal planning 2023–2027\nA High North Regional Forum between National and regional governments and Sami parliament\nCity growth agreements (‘byvekstavtaler\"’, on urban growth, land-use, transport and funding)\nRegional growth agreements (‘regionvekstavtaler’, under development)\nRural growth agreements (‘bygdevekstavtaler’, under development)\nCentral government planning guidelines for coordinated land-use and transport planning (2014), new version on the way\nCentral government planning guidelines for climate and energy planning and adaption (2018)\nCentral government planning guidelines for differentiated management for the beach zone (2021)\n\n\nPolicy co-ordination tools at regional level\nRegional plans\nRegional partnerships\nRegional forums for planning\n\n\nEvaluation and monitoring tools\nInstrument specific evaluation at national and regional level\nBiannual monitoring of regional development (Regionale utviklingstrekk 2021 - regjeringen.no), new version on the way.\n\n\nFuture orientations of regional policy\nThe 2023 White Paper Meld. St. 28 (2022–2023) Good urban communities with small inequalities (see below)\nA new white paper on living conditions in cities and city regions is expected in 2023\nA new White Paper on Housing is expected in 2024"
+ "text": "Overview\n\n\n\n Population and territory 5 425 270 (as of January 1, 2022) 323 810 km2 Administrative structure Unitary country Regional or state-level governments 11 Regions (fylker) Intermediate-level governments Municipal-level governments 356 Municipalities (kommuner) Share of subnational government in total expenditure/revenues (2021) 33.0% of total expenditure 27.7% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Norwegian regional issues are characterized by targeting areas with very low population density and limited accessibility to jobs and services. Regional disparities in income and unemployment are modest but labour and skills shortages and the age ratio in peripheral areas (‘distriktene’) have become pressing issues. Objectives of regional policy The goal of regional and rural (‘distrikt’) policy is ‘that people can live a good life throughout Norway, all local communities have room for development and economic growth, and increase in the population in rural municipalities. Ensure that people have access to work, housing and good services nearby. Facilitate safe, sustainable and vibrant local communities throughout the country through decentralized solutions. Legal/institutional framework for regional policy At the national level, the regional policy lead is the Department for Regional Development. Regional Policy Department has a coordinating role, working to ensure the priorities and measures of all sectors support regional development. Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) Budget allocation to rural (‘distrikt’) and regional policy comprises: - ‘narrow’ or targeted policies financed from the KDD with a budget in 2023 of NOK 1.3 billion - ‘broad’ measures financed from other budget lines, including other ministries (c. NOK 57 billion in 2023) In addition, there are large income equalization mechanisms between regions and municipalities National regional development policy framework The 2023 White Paper -– Meld. St. 27 (2022-23) ‘A good life throughout Norway - district policy for the future” Urban policy framework The 2017 White Paper ‘Urban sustainability and rural strength’ and the 2023 White Paper Meld. St. 28 (2022–2023) Good urban communities with small inequalities Rural policy framework The 2023 White Paper – Meld. St. 27 (2022-23) ‘A good life throughout Norway - district policy for the future Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Funding for ‘narrow’ regional policy (NOK 1.3 billion) to (i) Growing businesses, value creation and attractive labour markets in regions and districts; (ii) Regional development across national borders and in the High North; and (iii) Capacity building and basic services in the peripheral areas (‘distriktene’). Funding for ‘broad’ regional policy spend, amount to some NOK 57 billion comprises of A. Measures and arrangements that are based on rural (‘distrikt’) policy goals or that favour peripheral areas (‘distriktene’) beyond simple compensation to achieve equal opportunities - the regionally-differentiated social security concession - a package of (mainly tax) measures for the northern Troms and Finnmark ‘Action Zone’ - special measures for northern Norway (including a VAT exemption on energy from renewables) and grants for municipalities in southern Norway? B. Measures which aim to equalise or compensate between geographical areas and that are important for economic growth, employment or housing in rural areas. Category B comprises a range of sectoral measures including subsidies for land development, infrastructure, agriculture, cultural heritage and museums. Area-based urban initiatives: The Government has cooperation agreements with Urban municipalities with major challenges in living conditions in parts of the Citiies. Policy guidelines on location of public sector jobs and public services (state) Policy co-ordination tools at national level Ministry of Local Government and Regional Development (KDD) Coordination between KDD and other ministries State Secretary committee on high north policy Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) National expectations regarding regional and municipal planning 2023–2027 A High North Regional Forum between National and regional governments and Sami parliament City growth agreements (‘byvekstavtaler\"’, on urban growth, land-use, transport and funding) Regional growth agreements (‘regionvekstavtaler’, under development) Rural growth agreements (‘bygdevekstavtaler’, under development) Central government planning guidelines for coordinated land-use and transport planning (2014), new version on the way Central government planning guidelines for climate and energy planning and adaption (2018) Central government planning guidelines for differentiated management for the beach zone (2021) Policy co-ordination tools at regional level Regional plans Regional partnerships Regional forums for planning Evaluation and monitoring tools Instrument specific evaluation at national and regional level Biannual monitoring of regional development (Regionale utviklingstrekk 2021 - regjeringen.no), new version on the way. Future orientations of regional policy The 2023 White Paper Meld. St. 28 (2022–2023) Good urban communities with small inequalities (see below) A new white paper on living conditions in cities and city regions is expected in 2023 A new White Paper on Housing is expected in 2024"
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- "text": "Regional inequality trends\nNorway experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2000. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.076 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.006 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.325. For reference, the same value for OECD was 1.325. This gap decreased by 0.151 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.325 in 2020 and decreased by 0.151 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Norway, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2008 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 7 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Norway, between 2008 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nNorway experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2000. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.076 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.006 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.325. For reference, the same value for OECD was 1.325. This gap decreased by 0.151 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.325 in 2020 and decreased by 0.151 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Norway, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2008 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 7 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Norway, between 2008 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"title": "Poland",
"section": "Overview",
- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n37,827 thousand (June 30, 2022), 312.700 km2\n\n\n\n\nAdministrative structure \nUnitary\n\n\nRegional or state-level governments \n16 voivodeships\n\n\nIntermediate-level governments \n308 poviats plus 66 cities with the status of poviat\n\n\nMunicipal-level governments \n2489 gminas\n\n\nShare of subnational government in total expenditure/revenues (2021)\n32.1% of total expenditure\n34.9% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\nFollowing challenges for regional policy until 2030 have been identified:\n• Adapting to climate change and limiting risks for the environment.\n• Counteracting the negative effects of demographic processes.\n• Developing and supporting human and social capital.\n• Increasing the productivity and innovativeness of regional economies.\n• Developing infrastructure which increases competitiveness, investment attractiveness and living conditions in the regions.\n• Increasing the effectiveness of development management (including financing development activities) and cooperation between local governments and between sectors.\n• Counteracting territorial disparities and spatial concentration of development challenges and eliminating crisis situations in degraded areas.\n\n\nObjectives of regional policy\nIn line with the National Strategy for Regional Development 2030 the main objective of the regional policy in Poland is: the effective use of endogenous potentials of territories and their specialisation to achieve sustainable development of the country which will create conditions for the growth of income of Polish residents while achieving coherence in the social, economic, environmental and spatial dimensions. Three specific objectives were also defined.\n• Objective 1 Increasing the cohesion of country’s social, economic, environmental and spatial development.\n• Objective 2 Strengthening regional competitive advantages.\n• Objective 3 Improving quality of management and implementation of territorially targeted policies.\nThe main objective of the regional policy until 2030 will be implemented based on three specific objectives that complement one another. The role of the National Strategy for Regional Development 2030 is to connect and coordinate horizontal measures taken to implement objectives, which include strengthening the competitiveness of all regions, cities and rural areas (objectives 2 and 3) with objective 1 which ensures greater cohesion in the country’s development through providing support to areas that are economically weaker.\nThe regional policy until 2030 focuses actions on levelling up the living standard and development opportunities of medium-sized cities that struggle to cope with the effects of losing their industrial and administrative functions, and usually in rural areas – at risk of permanent marginalisation. It provides them with support that requires taking comprehensive measures tailored to the local character of actions. By supporting competitiveness of the regions, the policy assumes continued measures aimed at raising the quality of human and social capital and developing entrepreneurship and innovation. The strategy attaches great importance to developing competencies within public administration that are necessary for pursuing an effective development policy, in particular in territories with low development potential.\nHorizontal matters addressed by the objectives:\n• improving competitiveness of the regions based on making optimal use of their potential for development, having a proactive innovation policy, developing human and social capital in order to adjust their quality to the needs of the labour market and addressing infrastructural shortcomings,\n• improving access to public services, building a culture of solidarity, shared responsibility and cooperation,\n• improving the administrative potential and functioning of institutions, as well as their cooperation and active involvement in development activities.\n\n\nLegal/institutional framework for regional policy\nTreaty on the Functioning of the European Union, Article 174,\nNational Constitution\nAct on the principles of development policy,\nAct on the principles of implementing tasks financed from European funds in the financial perspective 2021-2027\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\nIn 2021 the state budget’s expenditures in the part 34 (regional development) amounted to PLN 2,083.6 million, while the expenditures of the Budżet środków europejskich (Budget of EU funds) under the part 34 of while the respective expenditures on PLN 23,180.2 million.\nAccording to the budgetary bill for 2022 the equalization part of the state budget’s subsidy for gminas was planned at the level of PLN 9,6 billion, with the equalization part of the subsidy for voivodeships was planned at PLN 2.3 billion.\n\n\nNational regional development policy framework\nNational Strategy for Regional Development 2030\nStrategy for Responsible Development for the period up to 2020 (including the perspective up to 2030)\nThe Partnership Agreement 2021-2027 for Poland covers 24 programmes (8 national programmes and 16 regional programmes) and 12 INTERREG programmes (concerning territorial cooperation). Cohesion Policy investments for 2021-2027 are planned in strong coordination with the National Recovery and Resilience Plan.\n\n\nUrban policy framework\nNational Urban Policy 2030\n\n\nRural policy framework\nThe Strategy for Sustainable Development of Rural Agriculture and Fisheries 2030\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\nRegional development strategies, supra-local development strategies, commune development strategies\nCohesion policy funds (Regional programmes 2021-2027, National programmes 2021-2027)\nDevelopment programmes and other instruments financed from national funds – the regional policy until 2030 using national public funds will be implemented through development programmes, including multi-annual programmes, which serve as an instrument for development strategy implementation.\nThe main mechanisms that strengthen the integrated approach to development and cooperation at local, regional and supra-regional level in the NSRD 2030: programming contract, sectoral contract and territorial agreement.\n\n\nPolicy co-ordination tools at national level\nCohesion policy funds (National programmes 2021-2027)\nStrategic projects\nDevelopment programmes\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\nEuropean Structural Funds and national co-funding\nNational programmes\nSpecial Economic Zones\nMonitoring committees for regional and national programmes\nPartnership Agreement Committee\nNational Territorial Observatory\nRegional Territorial Observatory\nProgramme contract\nSectoral contract\nTerritorial agreement\nThe main entities supporting the implementation of regional policy are:\n1) the Coordinating Committee for Development Policy (CCDP) – a consultative and advisory body of the Prime Minister. The fundamental objective of the CCDP is to ensure coordination of the process of designing and implementing the development policy, as well as strategic monitoring and evaluation of the instruments serving its implementation.\n2) the sub-committee for territorial dimension takes over tasks related to monitoring the NSRD. It also provides a broad forum for discussion and expert background for the implementation and monitoring of the country's regional policy. Its tasks include coordination of sectoral policies and instruments financed from various sources, for the socio-economic development of the country; formulation of recommendations concerning effectiveness, efficiency and usefulness of implemented intervention and applied instruments in sectoral policies and regional policy (including individual regions); co-ordination and formulation of recommendations concerning strategic projects indicated in the National Strategy for Regional Development and implemented on the basis of other strategic documents at the national level with a significant territorial impact.\nJoint Central Government and Local Government Committee encompassing representatives of state units of the territorial government. The Committee develops economic and social priorities that condition development of communes, districts and regions, evaluates legal and financial circumstances for operation of the territorial government units and provide opinions on normative acts, programme documents and solutions related to the problems of the territorial government.\n\n\nPolicy co-ordination tools at regional level\nRegional programmes 2021-2027\nProgramming contract\nTerritorial agreement\nCo-ordination tools of regional policy implementation which have their source in the cohesion policy (ITI, CLLD, ATT)\nRegional development strategies\nCo-operation instruments inspired by the ITI mechanism (Local Government Contract), Regional Territorial Investments)\nRegional Territorial Observatories\nVoivodeship Regional Research Centres\nRegional Social Dialog Councils\n\n\nEvaluation and monitoring tools\nNSRD monitoring is closely linked to the monitoring of public policies within the national system of development management. The process of monitoring the NSRD provides information on the progress and effects of strategy implementation, at the same time contributing to the process of monitoring the Strategy for Responsible Development.\nThe minister competent for regional development is in charge of organising the NSRD monitoring process and overseeing its proper functioning, and for that purpose once a year draws up a report on regional development in Poland. The report is a fundamental element of monitoring the NSRD.\nPreparation of the report is coordinated by the National Territorial Observatory. In the process of monitoring the NSRD, the analytical and information infrastructure in the area pertaining to the socio-economic situation and processes in the country and in regions is provided by Statistics Poland. The analytical and monitoring system for regional policy also includes Regional Territorial Observatories and Voivodeship Regional Research Centres. In order to ensure the complementarity of undertaken monitoring activities, close cooperation will be pursued with units operating as part of the system for the evaluation of the cohesion policy, namely the National Evaluation Unit.\nSystem of territorial indicators and targets linked to the Partnership Agreement.\nRegional systems of evaluation and monitoring (Regional Territorial Observatories and Voivodeship Regional Research Centres).\n\n\nFuture orientations of regional policy\nIncreasing the territorial orientation of national and regional programming documents\nMainstream urban-rural linkages and functional approach in the urban, rural and regional development policy framework\nMaintaining the role of the cohesion policy as a policy close to citizens, actually using the development potentials of the regions\nStrengthening the system of multi-level development management between all levels: country - region - local level, in the field of programming, implementation and monitoring of development policies at each level of their implementation.\nGreater inclusion of sectoral policies in the territorial dimension of regional policy implementation."
+ "text": "Overview\n\n\n\n Population and territory 37,827 thousand (June 30, 2022), 312.700 km2 Administrative structure Unitary Regional or state-level governments 16 voivodeships Intermediate-level governments 308 poviats plus 66 cities with the status of poviat Municipal-level governments 2489 gminas Share of subnational government in total expenditure/revenues (2021) 32.1% of total expenditure 34.9% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges Following challenges for regional policy until 2030 have been identified: • Adapting to climate change and limiting risks for the environment. • Counteracting the negative effects of demographic processes. • Developing and supporting human and social capital. • Increasing the productivity and innovativeness of regional economies. • Developing infrastructure which increases competitiveness, investment attractiveness and living conditions in the regions. • Increasing the effectiveness of development management (including financing development activities) and cooperation between local governments and between sectors. • Counteracting territorial disparities and spatial concentration of development challenges and eliminating crisis situations in degraded areas. Objectives of regional policy In line with the National Strategy for Regional Development 2030 the main objective of the regional policy in Poland is: the effective use of endogenous potentials of territories and their specialisation to achieve sustainable development of the country which will create conditions for the growth of income of Polish residents while achieving coherence in the social, economic, environmental and spatial dimensions. Three specific objectives were also defined. • Objective 1 Increasing the cohesion of country’s social, economic, environmental and spatial development. • Objective 2 Strengthening regional competitive advantages. • Objective 3 Improving quality of management and implementation of territorially targeted policies. The main objective of the regional policy until 2030 will be implemented based on three specific objectives that complement one another. The role of the National Strategy for Regional Development 2030 is to connect and coordinate horizontal measures taken to implement objectives, which include strengthening the competitiveness of all regions, cities and rural areas (objectives 2 and 3) with objective 1 which ensures greater cohesion in the country’s development through providing support to areas that are economically weaker. The regional policy until 2030 focuses actions on levelling up the living standard and development opportunities of medium-sized cities that struggle to cope with the effects of losing their industrial and administrative functions, and usually in rural areas – at risk of permanent marginalisation. It provides them with support that requires taking comprehensive measures tailored to the local character of actions. By supporting competitiveness of the regions, the policy assumes continued measures aimed at raising the quality of human and social capital and developing entrepreneurship and innovation. The strategy attaches great importance to developing competencies within public administration that are necessary for pursuing an effective development policy, in particular in territories with low development potential. Horizontal matters addressed by the objectives: • improving competitiveness of the regions based on making optimal use of their potential for development, having a proactive innovation policy, developing human and social capital in order to adjust their quality to the needs of the labour market and addressing infrastructural shortcomings, • improving access to public services, building a culture of solidarity, shared responsibility and cooperation, • improving the administrative potential and functioning of institutions, as well as their cooperation and active involvement in development activities. Legal/institutional framework for regional policy Treaty on the Functioning of the European Union, Article 174, National Constitution Act on the principles of development policy, Act on the principles of implementing tasks financed from European funds in the financial perspective 2021-2027 Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) In 2021 the state budget’s expenditures in the part 34 (regional development) amounted to PLN 2,083.6 million, while the expenditures of the Budżet środków europejskich (Budget of EU funds) under the part 34 of while the respective expenditures on PLN 23,180.2 million. According to the budgetary bill for 2022 the equalization part of the state budget’s subsidy for gminas was planned at the level of PLN 9,6 billion, with the equalization part of the subsidy for voivodeships was planned at PLN 2.3 billion. National regional development policy framework National Strategy for Regional Development 2030 Strategy for Responsible Development for the period up to 2020 (including the perspective up to 2030) The Partnership Agreement 2021-2027 for Poland covers 24 programmes (8 national programmes and 16 regional programmes) and 12 INTERREG programmes (concerning territorial cooperation). Cohesion Policy investments for 2021-2027 are planned in strong coordination with the National Recovery and Resilience Plan. Urban policy framework National Urban Policy 2030 Rural policy framework The Strategy for Sustainable Development of Rural Agriculture and Fisheries 2030 Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) Regional development strategies, supra-local development strategies, commune development strategies Cohesion policy funds (Regional programmes 2021-2027, National programmes 2021-2027) Development programmes and other instruments financed from national funds – the regional policy until 2030 using national public funds will be implemented through development programmes, including multi-annual programmes, which serve as an instrument for development strategy implementation. The main mechanisms that strengthen the integrated approach to development and cooperation at local, regional and supra-regional level in the NSRD 2030: programming contract, sectoral contract and territorial agreement. Policy co-ordination tools at national level Cohesion policy funds (National programmes 2021-2027) Strategic projects Development programmes Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) European Structural Funds and national co-funding National programmes Special Economic Zones Monitoring committees for regional and national programmes Partnership Agreement Committee National Territorial Observatory Regional Territorial Observatory Programme contract Sectoral contract Territorial agreement The main entities supporting the implementation of regional policy are: 1) the Coordinating Committee for Development Policy (CCDP) – a consultative and advisory body of the Prime Minister. The fundamental objective of the CCDP is to ensure coordination of the process of designing and implementing the development policy, as well as strategic monitoring and evaluation of the instruments serving its implementation. 2) the sub-committee for territorial dimension takes over tasks related to monitoring the NSRD. It also provides a broad forum for discussion and expert background for the implementation and monitoring of the country's regional policy. Its tasks include coordination of sectoral policies and instruments financed from various sources, for the socio-economic development of the country; formulation of recommendations concerning effectiveness, efficiency and usefulness of implemented intervention and applied instruments in sectoral policies and regional policy (including individual regions); co-ordination and formulation of recommendations concerning strategic projects indicated in the National Strategy for Regional Development and implemented on the basis of other strategic documents at the national level with a significant territorial impact. Joint Central Government and Local Government Committee encompassing representatives of state units of the territorial government. The Committee develops economic and social priorities that condition development of communes, districts and regions, evaluates legal and financial circumstances for operation of the territorial government units and provide opinions on normative acts, programme documents and solutions related to the problems of the territorial government. Policy co-ordination tools at regional level Regional programmes 2021-2027 Programming contract Territorial agreement Co-ordination tools of regional policy implementation which have their source in the cohesion policy (ITI, CLLD, ATT) Regional development strategies Co-operation instruments inspired by the ITI mechanism (Local Government Contract), Regional Territorial Investments) Regional Territorial Observatories Voivodeship Regional Research Centres Regional Social Dialog Councils Evaluation and monitoring tools NSRD monitoring is closely linked to the monitoring of public policies within the national system of development management. The process of monitoring the NSRD provides information on the progress and effects of strategy implementation, at the same time contributing to the process of monitoring the Strategy for Responsible Development. The minister competent for regional development is in charge of organising the NSRD monitoring process and overseeing its proper functioning, and for that purpose once a year draws up a report on regional development in Poland. The report is a fundamental element of monitoring the NSRD. Preparation of the report is coordinated by the National Territorial Observatory. In the process of monitoring the NSRD, the analytical and information infrastructure in the area pertaining to the socio-economic situation and processes in the country and in regions is provided by Statistics Poland. The analytical and monitoring system for regional policy also includes Regional Territorial Observatories and Voivodeship Regional Research Centres. In order to ensure the complementarity of undertaken monitoring activities, close cooperation will be pursued with units operating as part of the system for the evaluation of the cohesion policy, namely the National Evaluation Unit. System of territorial indicators and targets linked to the Partnership Agreement. Regional systems of evaluation and monitoring (Regional Territorial Observatories and Voivodeship Regional Research Centres). Future orientations of regional policy Increasing the territorial orientation of national and regional programming documents Mainstream urban-rural linkages and functional approach in the urban, rural and regional development policy framework Maintaining the role of the cohesion policy as a policy close to citizens, actually using the development potentials of the regions Strengthening the system of multi-level development management between all levels: country - region - local level, in the field of programming, implementation and monitoring of development policies at each level of their implementation. Greater inclusion of sectoral policies in the territorial dimension of regional policy implementation."
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- "text": "Regional inequality trends\nPoland experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2019. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.045 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.954. For reference, the same value for OECD was 1.475. This gap increased by 0.004 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.426. For reference, the same value for OECD was 1.325. This gap increased by 0.007 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.306 in 2020 and increased by 0.016 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Poland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 17 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Poland, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nPoland experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2019. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.045 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.954. For reference, the same value for OECD was 1.475. This gap increased by 0.004 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.426. For reference, the same value for OECD was 1.325. This gap increased by 0.007 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.306 in 2020 and increased by 0.016 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Poland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 17 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Poland, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"title": "Portugal",
"section": "Overview",
- "text": "Overview\n\n\n\n\n\n\n\n\n\nPopulation and territory\n10.352.042 (2021); 92.225,61 Km2.\n\n\n\n\nAdministrative structure \nUnitary country.\n\n\nRegional or state-level governments \n2 Autonomous Regions (“Regiões Autónomas”).\n\n\nIntermediate-level governments \n---\n\n\nMunicipal-level governments \n308 municipalities (of which: 278 in mainland, 19 in RA Azores and 11 in RA Madeira).\n\n\nShare of subnational government in total expenditure/revenues (2021)\n14.5% of total expenditure\n14.8% of total revenues\n[Source: Subnational governments in OECD countries: key data, 2023 edition]\n\n\nKey regional development challenges\n• The competitiveness and inclusive and sustainable growth in all regions.\n• Regional disparities between coastal areas (including the 2 metropolitan areas) and the inland with low density of population, economic activity, and broadband infrastructure.\n• Increasing ageing and depopulation, also impacting the urban centres.\n• Delegation of competences to CCDR (DL n.º 36/2023) and an ongoing decentralization process from central government to municipalities.\n• Implementing efficient multi-level governance systems.\n• The capacity gaps for new competences and new models of delivering policies.\n\n\nObjectives of regional policy\n• Enhancing the regional attractiveness and competitiveness to improve inclusive and sustainable growth for all regions, notably promoting the endogenous resources and products, but also diversifying the economic base.\n• Increase territorial cohesion between regions and within metropolitan areas by reducing the economic and social regional and intra-regional disparities, taking into account the demographic challenges.\n• Enhance the double transition (energy and digital).\n• Promote capacity building at all levels of administration.\n\n\nLegal/institutional framework for regional policy\n• Portuguese Republic Constitution, Article 6 and articles 225-262.\n• Strategy Portugal 2030 (RCM nº 98/2020)\n• Treaty on the Functioning of the European Union, Article 174.\n\n\nBudget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any)\n• Cohesion Policy Partnership Agreement 2021-2027: €23 billion of EU Structural Funds (ERDF, ESF+, JTF, EMFAF) and €8,5 billion of national co-financing.\n• National Recovery and Resilience Plan (NRRP) in its territorial dimension.\n• Decentralization Financing Fund (2023): €1,2 billion.\n\n\nNational regional development policy framework\n• The Partnership Agreement 2021-2027 for Portugal covers 12 programmes (5 thematic programmes and 7 regional programmes) and 10 INTERREG programmes (concerning European Territorial Cooperation).\n• Cohesion Policy investments for 2021-2027 are planned in strong coordination with the National Recovery and Resilience Plan (PRR).\n\n\nUrban policy framework\n• National Spatial Planning Policy Program (PNPOT).\n• Regional Plans for Spatial Planning (PROT).\n\n\nRural policy framework\n• CAP Strategic Plan 2023-2027.\n• National Spatial Planning Policy Program (PNPOT), Regional Plans for Spatial Planning (PROT).\n• Program of the Valorisation of the Inland (PVI) (RCM nº 18/2020).\n• Common Strategy for Cross-border Cooperation (ECDT).\n\n\nMajor regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.)\n• European Structural Funds and national co-funding.\n• Regional State Aid.\n\n\nPolicy co-ordination tools at national level\n• Presidency of Council of Ministers, Ministry for Territorial Cohesion (MCT), State Secretary for Regional Development (SEDR).\n• Interministerial Coordination Commission (CIC) Portugal 2030.\n• Territorial Consultation/Coordination Council (Conselho de Concertação Territorial - CCT).\n• Development and Cohesion Agency (AD&C).\n\n\nMulti-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.)\n• Territorial Consultation/Coordination Council (CCT).\n• Decentralization Monitoring Commission.\n• Monitoring Committees of EU Structural Funds Programmes, representing the national, regional/local government, civil stakeholders, and the managing authorities of Programmes in each region.\n• Regional Dynamics Network (RDR).\n\n\nPolicy co-ordination tools at regional level\n• Regional Councils.\n• Regional Coordination and Development Commissions (CCDR).\n• Regional Programmes (Cohesion Policy).\n• Regional Smart Specialisation Strategies (RIS3).\n• Territorial Instruments (e.g. ITI – Integrated Territorial Investments, Thematic ITI, Leader, etc.).\n\n\nEvaluation and monitoring tools\n• Reports on Territorial Instruments and Municipal Investments.\n• Monitoring and evaluation plans linked to the European Structural Funds (EU regulation 2021/1060).\n• Monitoring and Evaluation Network (RMA).\n• Mandatory midterm review of all programmes of EU structural funds in 2025.\n\n\nFuture orientations of regional policy\n• Portuguese government programme (2022-2026) has a chapter on the strategic challenge related to inequalities. It states the need to correct regional assymetries and to promote territorial cohesion.\n• Administrative Decentralisation Reform to the Municipalities (Law n.º 50/2018, 16th of August) and related legislation.\n• Administrative Deconcentration Reform to the CCDR."
+ "text": "Overview\n\n\n\n Population and territory 10.352.042 (2021); 92.225,61 Km2. Administrative structure Unitary country. Regional or state-level governments 2 Autonomous Regions (“Regiões Autónomas”). Intermediate-level governments --- Municipal-level governments 308 municipalities (of which: 278 in mainland, 19 in RA Azores and 11 in RA Madeira). Share of subnational government in total expenditure/revenues (2021) 14.5% of total expenditure 14.8% of total revenues [Source: Subnational governments in OECD countries: key data, 2023 edition] Key regional development challenges • The competitiveness and inclusive and sustainable growth in all regions. • Regional disparities between coastal areas (including the 2 metropolitan areas) and the inland with low density of population, economic activity, and broadband infrastructure. • Increasing ageing and depopulation, also impacting the urban centres. • Delegation of competences to CCDR (DL n.º 36/2023) and an ongoing decentralization process from central government to municipalities. • Implementing efficient multi-level governance systems. • The capacity gaps for new competences and new models of delivering policies. Objectives of regional policy • Enhancing the regional attractiveness and competitiveness to improve inclusive and sustainable growth for all regions, notably promoting the endogenous resources and products, but also diversifying the economic base. • Increase territorial cohesion between regions and within metropolitan areas by reducing the economic and social regional and intra-regional disparities, taking into account the demographic challenges. • Enhance the double transition (energy and digital). • Promote capacity building at all levels of administration. Legal/institutional framework for regional policy • Portuguese Republic Constitution, Article 6 and articles 225-262. • Strategy Portugal 2030 (RCM nº 98/2020) • Treaty on the Functioning of the European Union, Article 174. Budget allocated to regional development (i.e., amount) and fiscal equalisation mechanisms between jurisdictions (if any) • Cohesion Policy Partnership Agreement 2021-2027: €23 billion of EU Structural Funds (ERDF, ESF+, JTF, EMFAF) and €8,5 billion of national co-financing. • National Recovery and Resilience Plan (NRRP) in its territorial dimension. • Decentralization Financing Fund (2023): €1,2 billion. National regional development policy framework • The Partnership Agreement 2021-2027 for Portugal covers 12 programmes (5 thematic programmes and 7 regional programmes) and 10 INTERREG programmes (concerning European Territorial Cooperation). • Cohesion Policy investments for 2021-2027 are planned in strong coordination with the National Recovery and Resilience Plan (PRR). Urban policy framework • National Spatial Planning Policy Program (PNPOT). • Regional Plans for Spatial Planning (PROT). Rural policy framework • CAP Strategic Plan 2023-2027. • National Spatial Planning Policy Program (PNPOT), Regional Plans for Spatial Planning (PROT). • Program of the Valorisation of the Inland (PVI) (RCM nº 18/2020). • Common Strategy for Cross-border Cooperation (ECDT). Major regional policy tools (e.g., funds, plans, policy initiatives, institutional agreements, etc.) • European Structural Funds and national co-funding. • Regional State Aid. Policy co-ordination tools at national level • Presidency of Council of Ministers, Ministry for Territorial Cohesion (MCT), State Secretary for Regional Development (SEDR). • Interministerial Coordination Commission (CIC) Portugal 2030. • Territorial Consultation/Coordination Council (Conselho de Concertação Territorial - CCT). • Development and Cohesion Agency (AD&C). Multi-level governance mechanisms between national and subnational levels (e.g., institutional agreements, Committees, etc.) • Territorial Consultation/Coordination Council (CCT). • Decentralization Monitoring Commission. • Monitoring Committees of EU Structural Funds Programmes, representing the national, regional/local government, civil stakeholders, and the managing authorities of Programmes in each region. • Regional Dynamics Network (RDR). Policy co-ordination tools at regional level • Regional Councils. • Regional Coordination and Development Commissions (CCDR). • Regional Programmes (Cohesion Policy). • Regional Smart Specialisation Strategies (RIS3). • Territorial Instruments (e.g. ITI – Integrated Territorial Investments, Thematic ITI, Leader, etc.). Evaluation and monitoring tools • Reports on Territorial Instruments and Municipal Investments. • Monitoring and evaluation plans linked to the European Structural Funds (EU regulation 2021/1060). • Monitoring and Evaluation Network (RMA). • Mandatory midterm review of all programmes of EU structural funds in 2025. Future orientations of regional policy • Portuguese government programme (2022-2026) has a chapter on the strategic challenge related to inequalities. It states the need to correct regional assymetries and to promote territorial cohesion. • Administrative Decentralisation Reform to the Municipalities (Law n.º 50/2018, 16th of August) and related legislation. • Administrative Deconcentration Reform to the CCDR."
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- "text": "Regional inequality trends\nPortugal experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2000. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.148 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.15 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.481. For reference, the same value for OECD was 1.475. This gap decreased by 0.302 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.236. For reference, the same value for OECD was 1.325. This gap decreased by 0.142 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.062 in 2020 and decreased by 0.005 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \nIn Portugal, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 22 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Portugal, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nPortugal experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2000. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.148 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.15 higher in the same period, indicating bottom convergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.481. For reference, the same value for OECD was 1.475. This gap decreased by 0.302 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.236. For reference, the same value for OECD was 1.325. This gap decreased by 0.142 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.062 in 2020 and decreased by 0.005 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn Portugal, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 22 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Portugal, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
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"section": "Regional inequality trends",
- "text": "Regional inequality trends\n\n\n\n\n\n\nThe Slovak Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2009. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.884. For reference, the same value for OECD was 1.325. This gap decreased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn the Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nThe Slovak Republic experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2009. The figures are normalized, with values in the year 2000 set to 1.\nThe Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.\n\n\n\n\n\n\n\nNote: Top/bottom calculated as population equivalent (top/bottom regions with at least 20% of the population). The interpretation of top/bottom 20% GDP per capita is that 20% of the population in the country holds 20% of the value. Top 20%/Mean calculated as mean GDP per capita in top 20% regions over mean TL3 GDP per capita in a given year. Bottom 20%/Mean calculated as mean TL3 GDP per capita in bottom 20% regions over mean TL3 GDP per capita in a given year. To improve data consistency, input series are aggregated when TL3 regions are part of the same FUA. To improve time series, TL3 missing values have been estimated based on the evolution at higher geographic level. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nThere is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.884. For reference, the same value for OECD was 1.325. This gap decreased by 0.016 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland). Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn the Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022).\n\n \n\n\n\n\nTerritorial definitions\n\n\n\n\n\nThe data in this note reflect different sub-national geographic levels in OECD countries. In particular, regions are classified on two territorial levels reflecting the administrative organisation of countries: large regions (TL2) and small regions (TL3).\n\n\nSmall regions are classified according to their access to metropolitan areas (Fadic et al. 2019). The typology classifies small (TL3) regions into metropolitan and non-metropolitan regions according to the following criteria:\n\n\n\nMetropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.\n\n\nNon-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.\n\n\n\n\nDisclaimer: https://oecdcode.org/disclaimers/territories.html"
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"section": "Regional inequality trends",
- "text": "Regional inequality trends\n\n\n\n\n\n\nThe United States experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2010. The figures were normalized, with the values in the year 2000 set to 1.\nPolarisation, as measured by the Top 20%/Mean ratio was 0.063 higher in 2000 compared to 2020. Bottom divergence, as measured by the Bottom 20%/Mean ratio was 0.026 lower in the same period.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn the United States, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 24%, 1 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the United States, between 2001 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
+ "text": "Regional inequality trends\n\n\n\n\n\n\nThe United States experienced an increase in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2010. The figures were normalized, with the values in the year 2000 set to 1.\nPolarisation, as measured by the Top 20%/Mean ratio was 0.063 higher in 2000 compared to 2020. Bottom divergence, as measured by the Bottom 20%/Mean ratio was 0.026 lower in the same period.\n\n\n\n\n\n\n\nSource: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\n\n\n\nIn 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.307. For reference, the same value for OECD was 1.475. This gap decreased by 0.028 percentage points between 2000 and 2020.\nMeanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.126. For reference, the same value for OECD was 1.325. This gap decreased by 0.051 percentage points since 2000.\nIn turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.115 in 2020 and decrease by 0.06 percentage points since 2000.\n\n\n\n\n\n\n\nNote: Far from a FUA>250K includes regions near/with a small FUA and remote regions. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nIn the United States, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 24%, 1 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. Labour productivity in each group is equal to the sum of Gross Value Added, expressed in USD at constant prices and PPP (base year 2015) within the group, divided by the sum of total employment in regions within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Colombia, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Source: OECD Regional Database (2022).\n\n \n\n\n\n\n\n\nRegions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the United States, between 2001 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.\n\n\n\n\n\n\n\nNote: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N). Source: OECD Regional Database (2022)."
},
{
"objectID": "tl0-usa.html#recent-policy-developments",
diff --git a/docs/tl0-irl.html b/docs/tl0-irl.html
index 0ba5d34..592b4ef 100644
--- a/docs/tl0-irl.html
+++ b/docs/tl0-irl.html
@@ -370,8 +370,8 @@
Regional inequa
In Ireland, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 60 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -387,8 +387,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Ireland, between 2001 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.
diff --git a/docs/tl0-usa.html b/docs/tl0-usa.html
index bd3cfb6..d142f5f 100644
--- a/docs/tl0-usa.html
+++ b/docs/tl0-usa.html
@@ -370,8 +370,8 @@
Regional inequa
Polarisation, as measured by the Top 20%/Mean ratio was 0.063 higher in 2000 compared to 2020. Bottom divergence, as measured by the Bottom 20%/Mean ratio was 0.026 lower in the same period.
@@ -385,9 +385,15 @@
Regional inequa
+
+
+
In 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was 1.307. For reference, the same value for OECD was 1.475. This gap decreased by 0.028 percentage points between 2000 and 2020.
+
Meanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was 1.126. For reference, the same value for OECD was 1.325. This gap decreased by 0.051 percentage points since 2000.
+
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.115 in 2020 and decrease by 0.06 percentage points since 2000.
+
@@ -403,8 +409,8 @@
Regional inequa
In the United States, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 24%, 1 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -420,8 +426,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the United States, between 2001 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl2-can.html b/docs/tl2-can.html
index bdc34ec..9c58dd4 100644
--- a/docs/tl2-can.html
+++ b/docs/tl2-can.html
@@ -370,8 +370,8 @@
Regional inequa
Canada experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2004. The figures are normalized, with values in the year 2000 set to 1.
@@ -387,8 +387,8 @@
Regional inequa
In Canada, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 14% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -404,8 +404,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Canada, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl2-chl.html b/docs/tl2-chl.html
index 7ec2172..1d2f647 100644
--- a/docs/tl2-chl.html
+++ b/docs/tl2-chl.html
@@ -370,8 +370,8 @@
Regional inequa
Chile experienced a decline in the Theil index of GDP per capita over 2008-2020. Inequality reached its maximum in 2010. The figures are normalized, with values in the year 2008 set to 1.
@@ -387,8 +387,8 @@
Regional inequa
In Chile, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2013 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 5%, while it increased by 15% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -404,8 +404,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Chile, between 2013 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector remained approximately stable across all regions.
diff --git a/docs/tl2-mex.html b/docs/tl2-mex.html
index e835eab..2533ee4 100644
--- a/docs/tl2-mex.html
+++ b/docs/tl2-mex.html
@@ -370,8 +370,8 @@
Regional inequa
Mexico experienced a decline in the Theil index of GDP per capita over 2000-2020. Inequality reached its maximum in 2008. The figures are normalized, with values in the year 2000 set to 1.
@@ -387,8 +387,8 @@
Regional inequa
In Mexico, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2005 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 3%, while it increased by 6% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -404,8 +404,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Mexico, between 2005 and 2020, the share of workers in the industrial sector remained approximately stable across all regions. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions.
diff --git a/docs/tl3-aut.html b/docs/tl3-aut.html
index 0c588eb..bcb1869 100644
--- a/docs/tl3-aut.html
+++ b/docs/tl3-aut.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.114 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.069 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.143 in 2020 and decreased by 0.045 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Austria, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 9%, 9 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Austria, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it remained stable in the rest. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.
diff --git a/docs/tl3-bel.html b/docs/tl3-bel.html
index 9f5a9e6..92ad4c5 100644
--- a/docs/tl3-bel.html
+++ b/docs/tl3-bel.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.032 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.023 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.163 in 2020 and increased by 0.048 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Belgium, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 15%, 6 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Belgium, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-che.html b/docs/tl3-che.html
index d201744..3cba52d 100644
--- a/docs/tl3-che.html
+++ b/docs/tl3-che.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.028 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.018 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.183 in 2020 and decreased by 0.077 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Switzerland, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2011 and 2019. Over this period labour productivity grew roughly by 6% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Switzerland, between 2011 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-cze.html b/docs/tl3-cze.html
index 148ecee..c087d8a 100644
--- a/docs/tl3-cze.html
+++ b/docs/tl3-cze.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.054 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.313 in 2020 and increased by 0.122 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In the Czech Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 53%, 12 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Czech Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-deu.html b/docs/tl3-deu.html
index b5aaf2f..1bc5238 100644
--- a/docs/tl3-deu.html
+++ b/docs/tl3-deu.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.092 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.087 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.033 in 2020 and decreased by 0.089 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Germany, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 8%, 12 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Germany, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-dnk.html b/docs/tl3-dnk.html
index 38e33fd..2f27317 100644
--- a/docs/tl3-dnk.html
+++ b/docs/tl3-dnk.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.063 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.033 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.184 in 2020 and increased by 0.008 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Denmark, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 6 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Denmark, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-esp.html b/docs/tl3-esp.html
index 35b6298..46ec247 100644
--- a/docs/tl3-esp.html
+++ b/docs/tl3-esp.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.031 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.113 in 2020 and increased by 0.028 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Spain, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 11% in both groups of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Spain, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-est.html b/docs/tl3-est.html
index 57a39a6..d41063d 100644
--- a/docs/tl3-est.html
+++ b/docs/tl3-est.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.052 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.039 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.337 in 2020 and increased by 0.178 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Estonia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 57%, 38 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Estonia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in regions already located in the upper half of the labour productivity distribution while it went down in regions located in the lower half. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors widened the labour productivity gap between regions.
diff --git a/docs/tl3-fin.html b/docs/tl3-fin.html
index b568e3d..44cda79 100644
--- a/docs/tl3-fin.html
+++ b/docs/tl3-fin.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.052 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.134 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.236 in 2020 and decreased by 0.091 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Finland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 5%, 10 percentage points less than in the lower half of regions. During 2020, the gap remained stable. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Finland, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-fra.html b/docs/tl3-fra.html
index 0b28ec7..b0f7700 100644
--- a/docs/tl3-fra.html
+++ b/docs/tl3-fra.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.078 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.004 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.201 in 2020 and increased by 0.017 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In France, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 15%, 2 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In France, between 2001 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-gbr.html b/docs/tl3-gbr.html
index 692b27b..39e4f95 100644
--- a/docs/tl3-gbr.html
+++ b/docs/tl3-gbr.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.061 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio did not change in the same period.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.16 in 2020 and decreased by 0.031 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In the United Kingdom, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2004 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 10%, 3 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the United Kingdom, between 2004 and 2020, the share of workers in the industrial sector went down in all regions, approximately by the same amount. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector reduced the labour productivity gap between regions.
diff --git a/docs/tl3-grc.html b/docs/tl3-grc.html
index 2cef8ef..69143a1 100644
--- a/docs/tl3-grc.html
+++ b/docs/tl3-grc.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.123 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.042 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.155 in 2020 and increased by 0.001 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Greece, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 11%, while it declined only by 8% in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Greece, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-hun.html b/docs/tl3-hun.html
index 735a3f1..4977d06 100644
--- a/docs/tl3-hun.html
+++ b/docs/tl3-hun.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.04 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.052 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.274 in 2020 and increased by 0.095 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Hungary, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 35%, 4 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Hungary, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-ita.html b/docs/tl3-ita.html
index 5c80c5e..84d114c 100644
--- a/docs/tl3-ita.html
+++ b/docs/tl3-ita.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.044 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.006 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.163 in 2020 and decreased by 0.043 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Italy, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions declined roughly by 6%, while it declined only by 4% in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Italy, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-jpn.html b/docs/tl3-jpn.html
index aecdf53..8c3e244 100644
--- a/docs/tl3-jpn.html
+++ b/docs/tl3-jpn.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.066 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.01 lower in the same period, indicating bottom divergence.
@@ -393,32 +393,14 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.045 in 2020 and increased by 0.026 percentage points since 2000.
Note: Far from a FUA>250K includes regions near/with a small FUA and remote regions. OECD mean gap based on 1 586 TL3 regions in 27 countries with available data (no TL3 data for Australia, Canada, Chile, Colombia, Costa Rica, Iceland, Ireland, Israel, Mexico, Luxembourg and Switzerland).
Source: OECD Regional Database (2022).
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Recent policy developments
diff --git a/docs/tl3-ltu.html b/docs/tl3-ltu.html
index c3119f6..23ae127 100644
--- a/docs/tl3-ltu.html
+++ b/docs/tl3-ltu.html
@@ -371,8 +371,8 @@ Regional inequa
The Top 20%/Mean ratio was 0.177 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.064 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.804 in 2020 and increased by 0.414 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Lithuania, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 89%, 12 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Lithuania, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-lva.html b/docs/tl3-lva.html
index 00cb55d..0edcced 100644
--- a/docs/tl3-lva.html
+++ b/docs/tl3-lva.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.018 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.083 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 2.065 in 2020 and decreased by 0.057 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Latvia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 79%, 42 percentage points less than in the lower half of regions. During 2020, the gap remained unchanged. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Latvia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-nld.html b/docs/tl3-nld.html
index 44ad52d..8a07759 100644
--- a/docs/tl3-nld.html
+++ b/docs/tl3-nld.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.006 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.053 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
There is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.
@@ -410,8 +410,8 @@
Regional inequa
In the Netherlands, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 14%, 4 percentage points more than in the lower half of regions. During 2020, the gap narrowed down. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Netherlands, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that used to be in the lower half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector widened the labour productivity gap between regions while the opposite was true for tradable services.
diff --git a/docs/tl3-nor.html b/docs/tl3-nor.html
index 5b10ee8..3344daa 100644
--- a/docs/tl3-nor.html
+++ b/docs/tl3-nor.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.076 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.006 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.325 in 2020 and decreased by 0.151 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Norway, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2008 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 18%, 7 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Norway, between 2008 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the industrial sector reduced the labour productivity gap between regions. At the same time, the share of workers in the tradable services sector went up in all regions, approximately by the same amount.
diff --git a/docs/tl3-nzl.html b/docs/tl3-nzl.html
index bf25b04..f437f7d 100644
--- a/docs/tl3-nzl.html
+++ b/docs/tl3-nzl.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.072 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.04 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.231 in 2020 and increased by 0.016 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In New Zealand, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 10 percentage points less than in the lower half of regions.
@@ -426,8 +426,8 @@
Regional inequa
diff --git a/docs/tl3-pol.html b/docs/tl3-pol.html
index 1c54e33..b3dd0ba 100644
--- a/docs/tl3-pol.html
+++ b/docs/tl3-pol.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.045 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.306 in 2020 and increased by 0.016 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Poland, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 66%, 17 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Poland, between 2001 and 2020, the share of workers in the industrial sector went down in regions that used to be located in the upper half of the labour productivity distribution while it went up in regions located in the lower half. At the same time, the share of workers in the tradable services sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors reduced the labour productivity gap between regions.
diff --git a/docs/tl3-prt.html b/docs/tl3-prt.html
index 69df5ea..3f71db3 100644
--- a/docs/tl3-prt.html
+++ b/docs/tl3-prt.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.148 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.15 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.062 in 2020 and decreased by 0.005 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Portugal, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 12%, 22 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Portugal, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-svk.html b/docs/tl3-svk.html
index cce6eb9..9aefbb6 100644
--- a/docs/tl3-svk.html
+++ b/docs/tl3-svk.html
@@ -300,9 +300,7 @@
@@ -350,12 +348,6 @@
Slovak Republic<
-
Regional inequality trends
@@ -368,8 +360,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.074 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.022 lower in the same period, indicating bottom divergence.
@@ -390,8 +382,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.813 in 2020 and increased by 0.067 percentage points since 2000.
@@ -407,8 +399,8 @@
Regional inequa
In the Slovak Republic, the gap between the upper and the lower half of regions in terms of labour productivity increased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 68%, 7 percentage points more than in the lower half of regions. During 2020, the gap continued to widen. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -424,20 +416,14 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In the Slovak Republic, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
Note: A region is in the “upper half” if labour productivity was above the country median in the first year with available data and “lower half” if productivity was below the country median. The share of workers in a given sector for a group of regions is defined as the sum of employment in that sector within the group divided by the sum of total employment within the group. Regions are small (TL3) regions, except for Australia, Canada, Chile, Ireland, Mexico, Norway, Switzerland, Türkiye and the United States where they are large (TL2) regions due to data availability. Industry includes the following tradable goods sectors: Mining and quarrying (B), Manufacturing (C), Electricity, gas, steam and air conditioning supply (D) and Water supply; sewerage; waste management and remediation activities (E) NACE macro sectors. Tradable services include Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific and technical activities (M), Administrative and support service activities (N).
Source: OECD Regional Database (2022).
-
-
-Recent policy developments
-
-
-
diff --git a/docs/tl3-svn.html b/docs/tl3-svn.html
index 7c9cdae..c30da25 100644
--- a/docs/tl3-svn.html
+++ b/docs/tl3-svn.html
@@ -371,8 +371,8 @@ Regional inequa
The Top 20%/Mean ratio was 0.079 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.009 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.001 in 2020 and increased by 0.068 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Slovenia, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2001 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 31%, 12 percentage points less than in the lower half of regions. During 2020, the gap widened again. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Slovenia, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that used to be in the upper half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/docs/tl3-swe.html b/docs/tl3-swe.html
index d462b1a..157f50b 100644
--- a/docs/tl3-swe.html
+++ b/docs/tl3-swe.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.011 higher in 2020 compared to 2000, indicating increased polarisation. The Bottom 20%/Mean ratio was 0.036 lower in the same period, indicating bottom divergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.263 in 2020 and increased by 0.014 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Sweden, the gap between the upper and the lower half of regions in terms of labour productivity remained stable between 2001 and 2019. Over this period labour productivity grew roughly by 27% in both groups of regions. During 2020, the gap remained stable. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Sweden, between 2001 and 2020, the share of workers in the industrial sector went down in all regions but more so in regions that were already in the lower half of the labour productivity distribution. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares both in the industrial and in the tradable services sectors widened the labour productivity gap between regions.
diff --git a/docs/tl3-tur.html b/docs/tl3-tur.html
index 627444e..d11d6c8 100644
--- a/docs/tl3-tur.html
+++ b/docs/tl3-tur.html
@@ -371,8 +371,8 @@
Regional inequa
The Top 20%/Mean ratio was 0.075 lower in 2020 compared to 2000, indicating decreased polarisation. The Bottom 20%/Mean ratio was 0.056 higher in the same period, indicating bottom convergence.
@@ -393,8 +393,8 @@
Regional inequa
In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was 1.183 in 2020 and decreased by 0.104 percentage points since 2000.
@@ -410,8 +410,8 @@
Regional inequa
In Turkey, the gap between the upper and the lower half of regions in terms of labour productivity decreased between 2009 and 2019. Over this period labour productivity in the upper half of regions grew roughly by 25%, 23 percentage points less than in the lower half of regions. During 2020, the gap continued to narrow. Nevertheless, more years of data are necessary to determine the long-term impact of the COVID-19 pandemic on labour productivity gaps in regions.
@@ -427,8 +427,8 @@
Regional inequa
Regions where the economic activity shifts towards tradable activities, such as industry and tradable services, tend to grow faster in terms of labour productivity. In Turkey, between 2009 and 2020, the share of workers in the industrial sector went up in regions that used to be located in the lower half of the labour productivity distribution while it remained stable in the rest. At the same time, the share of workers in the tradable services sector went up in all regions but more so in regions that were already in the upper half of the labour productivity distribution. Hence, the evolution of employment shares in the tradable services sector widened the labour productivity gap between regions while the opposite was true for the industrial sector.
diff --git a/index.qmd b/index.qmd
index 721b9d2..e70d10b 100644
--- a/index.qmd
+++ b/index.qmd
@@ -702,7 +702,10 @@ text_all <- dp2 %>%
# put fig3 title in black
fig3 <- fig3 %>%
layout(
- title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl0-irl.qmd b/tl0-irl.qmd
index dc0521e..fea83a7 100644
--- a/tl0-irl.qmd
+++ b/tl0-irl.qmd
@@ -362,7 +362,11 @@ fig2 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE)
fig2 <- fig2 %>%
layout(
- title = list(text = "Figure 2: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0),
+ title = list(text = "Figure 2: Share of workers in most productive (tradable) sectors,\nTL2 regions",
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial")),
margin = list(
l = 50, r = 50,
b = 50, t = 120,
diff --git a/tl0-usa.qmd b/tl0-usa.qmd
index 08be1f6..a6049f6 100644
--- a/tl0-usa.qmd
+++ b/tl0-usa.qmd
@@ -515,6 +515,26 @@ gdp_3_gap_txt <- ifelse(gdp_5_gap - gdp_5_gap_lag > 0, "increase", "decrease")
gdp_3_gap_pct <- abs(round(gdp_5_gap - gdp_5_gap_lag, 3))
```
+```{r next paragraph}
+next_paragraph <- if (any(ctry %in% c("LTU", "EST", "FIN", "LVA", "NZL", "NOR", "SVK", "SVN", "CHE"))) {
+ glue("There is no data for the gap in GDP per capita between large metropolitan and non-large metropolitan regions for 2000 and 2020.")
+} else {
+ glue("In 2020, the gap in GDP per capita between large metropolitan and non-large metropolitan regions was { gdp_gap }. For reference, the same value for OECD was { gdp_2_gap }. This gap { gdp_gap_txt } by { gdp_gap_pct } percentage points between 2000 and 2020.")
+}
+
+last_paragraph <- if (any(ctry %in% c("KOR", "NLD"))) {
+ glue("There is no data for the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants for 2000 and 2020.")
+} else {
+ glue("In turn, the gap in GDP per capita between regions near and far a Functional Urban Area (FUA) of more than 250 thousand inhabitants was { gdp_5_gap } in 2020 and { gdp_3_gap_txt } by { gdp_3_gap_pct} percentage points since 2000.")
+}
+```
+
+`r next_paragraph`
+
+Meanwhile, in 2020, the gap in GDP per capita between metropolitan and non-metropolitan regions was `r gdp_3_gap`. For reference, the same value for OECD was `r gdp_4_gap`. This gap `r paste(gdp_2_gap_txt, "by", gdp_2_gap_pct)` percentage points since 2000.
+
+`r last_paragraph`
+
```{r usa_fig2_3}
# no interactivity
# fig2
@@ -904,7 +924,11 @@ text_all <- dp2 %>%
# put fig4 title in black
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL2 regions",
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl2-can.qmd b/tl2-can.qmd
index 1410fcf..c29c811 100644
--- a/tl2-can.qmd
+++ b/tl2-can.qmd
@@ -696,7 +696,11 @@ text_all <- dp2 %>%
# put fig3 title in black
fig3 <- fig3 %>%
layout(
- title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions",
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl2-chl.qmd b/tl2-chl.qmd
index 2458d80..d99c258 100644
--- a/tl2-chl.qmd
+++ b/tl2-chl.qmd
@@ -391,6 +391,30 @@ hpdiff <- hpgrew - lpgrew
hpmore <- ifelse(hpdiff > 0, "more", "less")
+# fig2 <- dp11 %>%
+# rename(
+# value = value_country,
+# series = name
+# ) %>%
+# ggplot() +
+# geom_line(aes(x = time, y = value, color = series), linewidth = 1.2) +
+# # theme_oecd(base_size = 10) +
+# theme_minimal() +
+# scale_colour_manual(values = c("#177dc7","#508551")) +
+# labs(
+# x = "", y = "Labour productivity (2015 USD PPP)", colour = "",
+# title = "Figure 2: Evolution of labour productivity, TL2 regions"
+# ) +
+# scale_x_continuous(labels = as.character(yrs), breaks = yrs) +
+# scale_y_continuous(labels = scales::number_format())+
+# theme(title = element_text(family = "serif")) # windowsFonts()
+
+fig3_title <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "CHL"))) {
+ "Figure 2: Evolution of labour productivity,\nTL2 regions"
+} else {
+ "Figure 2: Evolution of labour productivity,\nTL2 regions"
+}
+
fig2 <- dp11 %>%
rename(
value = value_country,
@@ -399,15 +423,19 @@ fig2 <- dp11 %>%
ggplot() +
geom_line(aes(x = time, y = value, color = series), linewidth = 1.2) +
# theme_oecd(base_size = 10) +
+ # theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 10, 0))) +
theme_minimal() +
- scale_colour_manual(values = c("#177dc7","#508551")) +
+ # scale_colour_manual(values = clrs3[1:2]) +
+ scale_colour_manual(values = c("#508551", "#177dc7")) +
labs(
x = "", y = "Labour productivity (2015 USD PPP)", colour = "",
- title = "Figure 2: Evolution of labour productivity, TL2 regions"
+ title = fig3_title
) +
scale_x_continuous(labels = as.character(yrs), breaks = yrs) +
scale_y_continuous(labels = scales::number_format())
+
+
ctry3 <- if (any(ctry %in% c("USA", "GBR", "CZE", "SVK", "NLD"))) {
paste("The", ctry2)
} else {
@@ -700,7 +728,11 @@ text_all <- dp2 %>%
# put fig3 title in black
fig3 <- fig3 %>%
layout(
- title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions",
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl2-mex.qmd b/tl2-mex.qmd
index b20b935..3d9ae5d 100644
--- a/tl2-mex.qmd
+++ b/tl2-mex.qmd
@@ -689,7 +689,10 @@ text_all <- dp2 %>%
# put fig3 title in black
fig3 <- fig3 %>%
layout(
- title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = "Figure 3: Share of workers in most productive (tradable) sectors,\nTL2 regions", x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-aut.qmd b/tl3-aut.qmd
index d08dc5c..eda21d0 100644
--- a/tl3-aut.qmd
+++ b/tl3-aut.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r aut_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-bel.qmd b/tl3-bel.qmd
index 85b4af4..483b512 100644
--- a/tl3-bel.qmd
+++ b/tl3-bel.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r bel_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-che.qmd b/tl3-che.qmd
index 6f80ee7..0425f58 100644
--- a/tl3-che.qmd
+++ b/tl3-che.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r che_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-cze.qmd b/tl3-cze.qmd
index db17b74..4f7e3d2 100644
--- a/tl3-cze.qmd
+++ b/tl3-cze.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r cze_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-deu.qmd b/tl3-deu.qmd
index 58cb05a..e5cc2f6 100644
--- a/tl3-deu.qmd
+++ b/tl3-deu.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r deu_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-dnk.qmd b/tl3-dnk.qmd
index df87643..5b2f87b 100644
--- a/tl3-dnk.qmd
+++ b/tl3-dnk.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r dnk_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-esp.qmd b/tl3-esp.qmd
index 20d86f3..7cf9ae8 100644
--- a/tl3-esp.qmd
+++ b/tl3-esp.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r esp_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-est.qmd b/tl3-est.qmd
index 63f7fec..0c14864 100644
--- a/tl3-est.qmd
+++ b/tl3-est.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r est_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-fin.qmd b/tl3-fin.qmd
index 17e06e6..f3200d9 100644
--- a/tl3-fin.qmd
+++ b/tl3-fin.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r fin_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-fra.qmd b/tl3-fra.qmd
index 3603cf7..1df4089 100644
--- a/tl3-fra.qmd
+++ b/tl3-fra.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r fra_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-gbr.qmd b/tl3-gbr.qmd
index 2abaaf3..9426649 100644
--- a/tl3-gbr.qmd
+++ b/tl3-gbr.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r gbr_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-grc.qmd b/tl3-grc.qmd
index 337965d..28cedff 100644
--- a/tl3-grc.qmd
+++ b/tl3-grc.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r grc_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-hun.qmd b/tl3-hun.qmd
index 85c6d53..665525d 100644
--- a/tl3-hun.qmd
+++ b/tl3-hun.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r hun_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-ita.qmd b/tl3-ita.qmd
index dc4edc4..63d7bf3 100644
--- a/tl3-ita.qmd
+++ b/tl3-ita.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r ita_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-jpn.qmd b/tl3-jpn.qmd
index 7c42730..9bb199e 100644
--- a/tl3-jpn.qmd
+++ b/tl3-jpn.qmd
@@ -552,366 +552,6 @@ ggplotly(fig2) %>%
-```{r jpn_fig3, eval = FALSE}
-# read ----
-dp1 <- read_excel("data/countryprofile_fig3_alt.xlsx", sheet = ctry) %>%
- select(time, pw_lp, pw_hp) %>%
- clean_names()
-
-# tidy ----
-
-# colnames(dp1) <- str_replace(colnames(dp1), tolower(ctry), "country")
-
-colnames(dp1) <- c("time", "pw_lp_country", "pw_hp_country")
-
-dp11 <- dp1 %>%
- select(time, matches("country")) %>%
- pivot_longer(-time) %>%
- rename(value_country = value)
-
-dp11 <- dp11 %>%
- mutate(
- name = case_when(
- name == "pw_lp_country" ~ "Lower half",
- name == "pw_hp_country" ~ "Upper half"
- )
- )
-
-# plot ----
-
-yrs <- sort(unique(dp11$time))
-yrs <- seq(min(yrs), max(yrs), 2)
-
-hpgrew <- dp11 %>%
- filter(
- name == "Upper half",
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = 100 * (value_country - lag(value_country)) / lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-lpgrew <- dp11 %>%
- filter(
- name == "Lower half",
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = 100 * (value_country - lag(value_country)) / lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-hpdiff <- hpgrew - lpgrew
-
-hpmore <- ifelse(hpdiff > 0, "more", "less")
-
-fig3_title <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE"))) {
- "Figure 3: Evolution of labour productivity,\nTL2 regions"
-} else {
- "Figure 3: Evolution of labour productivity,\nTL3 regions"
-}
-
-fig3_1 <- dp11 %>%
- rename(
- value = value_country,
- series = name
- ) %>%
- ggplot() +
- geom_line(aes(x = time, y = value, color = series), linewidth = 1.2) +
- # theme_oecd(base_size = 10) +
- # theme(plot.title = element_text(size = 13, hjust = 0, margin = margin(0, 0, 10, 0))) +
- theme_minimal() +
- # scale_colour_manual(values = clrs3[1:2]) +
- scale_colour_manual(values = c("#508551", "#177dc7")) +
- labs(
- x = "", y = "Labour productivity (2015 USD PPP)", colour = "",
- title = fig3_title
- ) +
- scale_x_continuous(labels = as.character(yrs), breaks = yrs) +
- scale_y_continuous(labels = scales::number_format())
-```
-
-```{r jpn_fig3_text, eval = FALSE}
-fig3_text <- read_excel("data/fig3_text_FINAL.xlsx", sheet = "text") %>%
- clean_names()
-
-fig3_text <- fig3_text %>%
- filter(iso3 == ctry) %>%
- pull(text_final)
-```
-
-```{r jpn_fig3_2, eval = FALSE}
-# no interactivity
-# fig3_1
-
-# interactivity
-ggplotly(fig3_1) %>%
- config(displayModeBar = F)
-```
-
-```{r jpn_fig4, eval = FALSE}
-# read ----
-
-dp2 <- read_excel("data/countryprofile_fig4_alt.xlsx", sheet = ctry) %>%
- clean_names()
-
-# tidy ----
-
-dp21 <- dp2 %>%
- select(time, starts_with("share_")) %>%
- pivot_longer(-time) %>%
- rename(value_country = value)
-
-dp21 <- dp21 %>%
- mutate(
- name = case_when(
- name == "share_tgoods_lp" ~ paste(ctry, "TG LP"),
- name == "share_tgoods_hp" ~ paste(ctry, "TG HP"),
- name == "share_tserv_lp" ~ paste(ctry, "TS LP"),
- name == "share_tserv_hp" ~ paste(ctry, "TS HP")
- )
- )
-
-# plot ----
-
-yrs <- sort(unique(dp21$time))
-yrs <- seq(min(yrs), max(yrs), 1)
-
-tghpgrew <- dp21 %>%
- filter(
- name == paste(ctry, "TG HP"),
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = value_country - lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-tglpgrew <- dp21 %>%
- filter(
- name == paste(ctry, "TG LP"),
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = value_country - lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-tghpdiff <- tghpgrew - tglpgrew
-tghpmore <- ifelse(tghpdiff > 0, "grew", "declined")
-
-tglpdiff <- tglpgrew - tglpgrew
-tglpmore <- ifelse(tglpdiff > 0, "grew", "declined")
-
-tshpgrew <- dp21 %>%
- filter(
- name == paste(ctry, "TS HP"),
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = value_country - lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-tslpgrew <- dp21 %>%
- filter(
- name == paste(ctry, "TS LP"),
- time %in% c(min(yrs), max(yrs))
- ) %>%
- summarise(grew = value_country - lag(value_country)) %>%
- drop_na() %>%
- pull() %>%
- round(1)
-
-tshpdiff <- tshpgrew - tslpgrew
-tshpmore <- ifelse(tshpdiff > 0, "grew", "declined")
-
-tslpdiff <- tslpgrew - tslpgrew
-tslpmore <- ifelse(tslpdiff > 0, "grew", "declined")
-
-dp21 <- dp21 %>%
- mutate(category = gsub(paste0("^OECD |^", ctry, " "), "", name))
-
-dp21 <- dp21 %>%
- mutate(
- country = str_replace_all(name, " .*", ""),
- category1 = str_sub(category, 1, 2),
- category1 = str_replace_all(category1, "TG", "Tradable goods"),
- category1 = str_replace_all(category1, "TS", "Tradable services"),
- category2 = paste(str_sub(category, 4, 5), time),
- category2 = str_replace_all(category2, "LP", "Lower half"),
- category2 = str_replace_all(category2, "HP", "Upper half")
- )
-
-dp21_2 <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(
- category2_2 = category2,
- category2 = str_replace_all(category2, as.character(min(yrs)), "minyr"),
- category2 = str_replace_all(category2, as.character(max(yrs)), "maxyr")
- ) %>%
- pivot_wider(names_from = category2, values_from = value_country) %>%
- clean_names()
-
-# for plot_ly -----
-dp21_2 <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half"))
-
-dp21_min_TG <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
- filter(time == min(dp21_2$time) & category1 == "Tradable goods")
-
-dp21_min_TS <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
- filter(time == min(dp21_2$time) & category1 == "Tradable services")
-
-dp21_max_TG <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
- filter(time == max(dp21_2$time) & category1 == "Tradable goods")
-
-dp21_max_TS <- dp21 %>%
- select(country, time, category1, category2, value_country) %>%
- mutate(type = ifelse(str_detect(category2, "Lower"), "Lower half", "Upper half")) %>%
- filter(time == max(dp21_2$time) & category1 == "Tradable services")
-
-data_plotly <- tibble(
- "x" = c("Lower half", "Upper half"),
- "y" = dp21_min_TG$value_country,
- "y2" = dp21_min_TS$value_country,
- "y3" = dp21_max_TG$value_country,
- "y4" = dp21_max_TS$value_country,
- "name1" = dp21_min_TG$category2,
- "name2" = dp21_max_TG$category2
-)
-
-x <- c("Lower half", "Upper half")
-
-y <- dp21_min_TG$value_country
-y2 <- dp21_min_TS$value_country
-
-y3 <- dp21_max_TG$value_country
-y4 <- dp21_max_TS$value_country
-
-name1 <- dp21_min_TG$category2
-name2 <- dp21_max_TG$category2
-
-p1 <- plot_ly() %>% add_trace(
- x = ~x, y = ~y3, color = ~x,
- type = "bar",
- name = ~name2,
- marker = list(
- color = c("#c8f075","#6bc5f2")
- )
-)
-
-p1 <- p1 %>% add_markers(
- x = ~x, y = ~y, color = ~x,
- name = ~name1,
- mode = "markers",
- marker = list(
- color = clrs4[2:1],
- size = 12,
- symbol = "diamond-dot"
- )
-)
-
-p1 <- p1 %>% layout(
- title = "Tradable goods",
- xaxis = list(title = "", visible = FALSE),
- yaxis = list(title = "Employment share (%)")
-)
-
-p2 <- plot_ly() %>% add_trace(
- x = ~x, y = ~y4, color = ~x,
- type = "bar",
- marker = list(color = c("#c8f075","#6bc5f2")),
- showlegend = FALSE
-)
-
-p2 <- p2 %>% add_markers(
- x = ~x, y = ~y2, color = ~x,
- mode = "markers",
- marker = list(
- color = clrs4[2:1],
- size = 12,
- symbol = "diamond-dot"
- ),
- showlegend = FALSE
-)
-
-p2 <- p2 %>% layout(
- title = "Tradable services",
- xaxis = list(title = "", visible = FALSE),
- yaxis = list(title = "Employment share (%)")
-)
-
-fig4 <- subplot(p1, p2, nrows = 1, margin = 0.05, shareX = TRUE, shareY = TRUE)
-
-fig4 <- fig4 %>%
- layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors,\nTL3 regions", x = 0),
- margin = list(
- l = 50, r = 50,
- b = 50, t = 120,
- pad = 4
- ),
- annotations = list(
- list(
- x = 0.25,
- y = 1,
- font = list(size = 14),
- text = "Industry",
- xref = "paper",
- yref = "paper",
- xanchor = "center",
- yanchor = "bottom",
- showarrow = FALSE
- ),
- list(
- x = 0.75,
- y = 1,
- font = list(size = 14),
- text = "Tradable services",
- xref = "paper",
- yref = "paper",
- xanchor = "center",
- yanchor = "bottom",
- showarrow = FALSE
- )
- )
- )
-```
-
-```{r jpn_fig4_text, eval = FALSE}
-text_all <- dp2 %>%
- filter(time == 2020) %>%
- pull(text_all)
-```
-
-
-```{r jpn_fig4_2, eval = FALSE}
-# put fig4 title in black
-fig4 <- fig4 %>%
- layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
- )
-
-# remove legend background
-fig4 <- fig4 %>%
- layout(
- legend = list(bgcolor = "rgba(0,0,0,0)")
- )
-
-ggplotly(fig4) %>%
- config(displayModeBar = F)
-```
-
-
## Recent policy developments
```{r jpn_txt}
diff --git a/tl3-ltu.qmd b/tl3-ltu.qmd
index 0a1d4be..7bc6d83 100644
--- a/tl3-ltu.qmd
+++ b/tl3-ltu.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r ltu_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-lva.qmd b/tl3-lva.qmd
index 646e69f..95085bb 100644
--- a/tl3-lva.qmd
+++ b/tl3-lva.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r lva_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-nld.qmd b/tl3-nld.qmd
index a5da027..44c676c 100644
--- a/tl3-nld.qmd
+++ b/tl3-nld.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r nld_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-nor.qmd b/tl3-nor.qmd
index 04ff9b7..b285986 100644
--- a/tl3-nor.qmd
+++ b/tl3-nor.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r nor_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-nzl.qmd b/tl3-nzl.qmd
index f95d069..9cb4530 100644
--- a/tl3-nzl.qmd
+++ b/tl3-nzl.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r nzl_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-pol.qmd b/tl3-pol.qmd
index 3770f24..48e8093 100644
--- a/tl3-pol.qmd
+++ b/tl3-pol.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r pol_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-prt.qmd b/tl3-prt.qmd
index ed600d1..b4ba1d0 100644
--- a/tl3-prt.qmd
+++ b/tl3-prt.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r prt_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-svk.qmd b/tl3-svk.qmd
index fddbf83..302ca84 100644
--- a/tl3-svk.qmd
+++ b/tl3-svk.qmd
@@ -50,11 +50,6 @@ clrs5 <- tintin_colours$red_rackhams_treasure
-## Overview
-
-```{r svk_tbl, eval = FALSE}
-read_html_table(ctry)
-```
## Regional inequality trends
@@ -907,9 +902,22 @@ text_all <- dp2 %>%
```{r svk_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
@@ -930,11 +938,6 @@ ggplotly(fig4) %>%
-## Recent policy developments
-
-```{r svk_txt, eval = FALSE}
-read_html_text(ctry)
-```
diff --git a/tl3-svn.qmd b/tl3-svn.qmd
index 32fb76a..0df1eec 100644
--- a/tl3-svn.qmd
+++ b/tl3-svn.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r svn_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-swe.qmd b/tl3-swe.qmd
index 8257216..e2e9758 100644
--- a/tl3-swe.qmd
+++ b/tl3-swe.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r swe_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background
diff --git a/tl3-tur.qmd b/tl3-tur.qmd
index d0367d9..f0d2713 100644
--- a/tl3-tur.qmd
+++ b/tl3-tur.qmd
@@ -907,9 +907,22 @@ text_all <- dp2 %>%
```{r tur_fig4_2}
# put fig4 title in black
+
+title_fig4 <- if (any(ctry %in% c("USA", "TUR", "NOR", "CHE", "IRL"))) {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL2 regions"
+} else {
+ "Figure 4: Share of workers in most productive (tradable) sectors, \nTL3 regions"
+}
+
+# https://stackoverflow.com/questions/34610165/what-is-the-default-font-for-ggplot2
+# https://plotly.com/r/reference/layout/
fig4 <- fig4 %>%
layout(
- title = list(text = "Figure 4: Share of workers in most productive (tradable) sectors, TL2 regions", x = 0, font = list(color = "black"))
+ title = list(text = title_fig4,
+ x = 0,
+ xanchor = "left",
+ xref = "paper",
+ font = list(color = "black", family = "Arial"))
)
# remove legend background